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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
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : List[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A_ : str = {
'''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'''
),
},
}
A_ : List[str] = {
'''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'''
),
},
}
A_ : Optional[Any] = {
'''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'''
),
},
}
A_ : str = {
'''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
'''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
A_ : Dict = {
'''facebook/dpr-question_encoder-single-nq-base''': 5_12,
'''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
A_ : int = {
'''facebook/dpr-reader-single-nq-base''': 5_12,
'''facebook/dpr-reader-multiset-base''': 5_12,
}
A_ : Optional[Any] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
A_ : int = {
'''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
A_ : int = {
'''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
A_ : Optional[int] = collections.namedtuple(
'''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)
A_ : Union[str, Any] = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])
A_ : List[Any] = 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 _lowercase :
def __call__( self : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Union[bool, str] = False , __lowerCAmelCase : Union[bool, str] = False , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = None , **__lowerCAmelCase : int , ) -> BatchEncoding:
"""simple docstring"""
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:
a = 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 , )
a = titles if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) else [titles]
a = texts if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) else [texts]
a = len(__lowerCAmelCase )
a = 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.""" )
a = super().__call__(__lowerCAmelCase , __lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase )["input_ids"]
a = super().__call__(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , padding=__lowerCAmelCase , truncation=__lowerCAmelCase )["input_ids"]
a = {
"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:
a = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
a = attention_mask
return self.pad(__lowerCAmelCase , padding=__lowerCAmelCase , max_length=__lowerCAmelCase , return_tensors=__lowerCAmelCase )
def A ( self : Optional[int] , __lowerCAmelCase : BatchEncoding , __lowerCAmelCase : DPRReaderOutput , __lowerCAmelCase : int = 16 , __lowerCAmelCase : int = 64 , __lowerCAmelCase : int = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
a = reader_input["input_ids"]
a , a , a = reader_output[:3]
a = len(__lowerCAmelCase )
a = sorted(range(__lowerCAmelCase ) , reverse=__lowerCAmelCase , key=relevance_logits.__getitem__ )
a = []
for doc_id in sorted_docs:
a = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
a = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
a = sequence_ids.index(self.pad_token_id )
else:
a = len(__lowerCAmelCase )
a = 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 : List[str] , __lowerCAmelCase : List[int] , __lowerCAmelCase : List[int] , __lowerCAmelCase : int , __lowerCAmelCase : int , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
a = []
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) )
a = sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] , reverse=__lowerCAmelCase )
a = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""" )
a = 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 _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = READER_PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = READER_PRETRAINED_INIT_CONFIGURATION
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
| 32
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 1
|
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : List[str] , __lowerCAmelCase : TransformeraDModel , __lowerCAmelCase : AutoencoderKL , __lowerCAmelCase : KarrasDiffusionSchedulers , __lowerCAmelCase : Optional[Dict[int, str]] = None , ) -> List[str]:
"""simple docstring"""
super().__init__()
self.register_modules(transformer=__lowerCAmelCase , vae=__lowerCAmelCase , scheduler=__lowerCAmelCase )
# create a imagenet -> id dictionary for easier use
a = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
a = int(__lowerCAmelCase )
a = dict(sorted(self.labels.items() ) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Union[str, List[str]] ) -> List[int]:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = list(__lowerCAmelCase )
for l in label:
if l not in self.labels:
raise ValueError(
f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""" )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : Optional[int] , __lowerCAmelCase : List[int] , __lowerCAmelCase : float = 4.0 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : int = 50 , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = len(__lowerCAmelCase )
a = self.transformer.config.sample_size
a = self.transformer.config.in_channels
a = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=__lowerCAmelCase , device=self.device , dtype=self.transformer.dtype , )
a = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
a = torch.tensor(__lowerCAmelCase , device=self.device ).reshape(-1 )
a = torch.tensor([1000] * batch_size , device=self.device )
a = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(__lowerCAmelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
a = latent_model_input[: len(__lowerCAmelCase ) // 2]
a = torch.cat([half, half] , dim=0 )
a = self.scheduler.scale_model_input(__lowerCAmelCase , __lowerCAmelCase )
a = t
if not torch.is_tensor(__lowerCAmelCase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
a = latent_model_input.device.type == "mps"
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = torch.floataa if is_mps else torch.floataa
else:
a = torch.intaa if is_mps else torch.intaa
a = torch.tensor([timesteps] , dtype=__lowerCAmelCase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
a = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
a = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
a = self.transformer(
__lowerCAmelCase , timestep=__lowerCAmelCase , class_labels=__lowerCAmelCase ).sample
# perform guidance
if guidance_scale > 1:
a , a = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
a , a = torch.split(__lowerCAmelCase , len(__lowerCAmelCase ) // 2 , dim=0 )
a = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
a = torch.cat([half_eps, half_eps] , dim=0 )
a = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
a , a = torch.split(__lowerCAmelCase , __lowerCAmelCase , dim=1 )
else:
a = noise_pred
# compute previous image: x_t -> x_t-1
a = self.scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
if guidance_scale > 1:
a , a = latent_model_input.chunk(2 , dim=0 )
else:
a = latent_model_input
a = 1 / self.vae.config.scaling_factor * latents
a = self.vae.decode(__lowerCAmelCase ).sample
a = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
a = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 1
|
import copy
import re
class _lowercase :
_UpperCAmelCase = '''hp'''
_UpperCAmelCase = {}
_UpperCAmelCase = None
@classmethod
def A ( cls : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
a = prefix
a = defaults
cls.build_naming_info()
@staticmethod
def A ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
if len(__lowerCAmelCase ) == 0:
return ""
a = None
if any(char.isdigit() for char in word ):
raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number""" )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(__lowerCAmelCase ) + 1 ):
a = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
a = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(__lowerCAmelCase : Union[str, Any] ):
a = ""
while integer != 0:
a = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
a = 0
while True:
a = word + "#" + int_to_alphabetic(__lowerCAmelCase )
if sword in info["reverse_short_word"]:
continue
else:
a = sword
break
a = short_word
a = word
return short_word
@staticmethod
def A ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = param_name.split("_" )
a = [TrialShortNamer.shortname_for_word(__lowerCAmelCase , __lowerCAmelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
a = ["", "_"]
for separator in separators:
a = separator.join(__lowerCAmelCase )
if shortname not in info["reverse_short_param"]:
a = shortname
a = param_name
return shortname
return param_name
@staticmethod
def A ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
a = TrialShortNamer.shortname_for_key(__lowerCAmelCase , __lowerCAmelCase )
a = short_name
a = param_name
@classmethod
def A ( cls : Tuple ) -> List[Any]:
"""simple docstring"""
if cls.NAMING_INFO is not None:
return
a = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
a = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(__lowerCAmelCase , __lowerCAmelCase )
a = info
@classmethod
def A ( cls : int , __lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
cls.build_naming_info()
assert cls.PREFIX is not None
a = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f"""You should provide a default value for the param name {k} with value {v}""" )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
a = cls.NAMING_INFO["short_param"][k]
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = 1 if v else 0
a = "" if isinstance(__lowerCAmelCase , (int, float) ) else "-"
a = f"""{key}{sep}{v}"""
name.append(__lowerCAmelCase )
return "_".join(__lowerCAmelCase )
@classmethod
def A ( cls : List[Any] , __lowerCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
a = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
a = []
else:
a = repr.split("_" )
a = {}
for value in values:
if "-" in value:
a , a = value.split("-" )
else:
a = re.sub("[0-9.]" , "" , __lowerCAmelCase )
a = float(re.sub("[^0-9.]" , "" , __lowerCAmelCase ) )
a = cls.NAMING_INFO["reverse_short_param"][p_k]
a = p_v
for k in cls.DEFAULTS:
if k not in parameters:
a = cls.DEFAULTS[k]
return parameters
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 1
|
import doctest
from collections import deque
import numpy as np
class _lowercase :
def __init__( self : Optional[Any] ) -> None:
"""simple docstring"""
a = [2, 1, 2, -1]
a = [1, 2, 3, 4]
def A ( self : Union[str, Any] ) -> list[float]:
"""simple docstring"""
a = len(self.first_signal )
a = len(self.second_signal )
a = max(__lowerCAmelCase , __lowerCAmelCase )
# create a zero matrix of max_length x max_length
a = [[0] * max_length for i in range(__lowerCAmelCase )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(__lowerCAmelCase ):
a = deque(self.second_signal )
rotated_signal.rotate(__lowerCAmelCase )
for j, item in enumerate(__lowerCAmelCase ):
matrix[i][j] += item
# multiply the matrix with the first signal
a = np.matmul(np.transpose(__lowerCAmelCase ) , np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(__lowerCAmelCase , 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 32
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Tuple = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = {}
a = tokenizer(example["content"] , truncation=UpperCAmelCase__ )["input_ids"]
a = len(example["content"] ) / len(output["input_ids"] )
return output
A_ : Tuple = HfArgumentParser(PretokenizationArguments)
A_ : List[str] = parser.parse_args()
if args.num_workers is None:
A_ : Union[str, Any] = multiprocessing.cpu_count()
A_ : Tuple = AutoTokenizer.from_pretrained(args.tokenizer_dir)
A_ : Optional[Any] = time.time()
A_ : Dict = load_dataset(args.dataset_name, split='''train''')
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")
A_ : Union[str, Any] = time.time()
A_ : str = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")
A_ : Optional[Any] = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")
| 32
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 1
|
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_UpperCAmelCase = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def UpperCAmelCase__ ( ):
'''simple docstring'''
if os.name == "nt":
a = CursorInfo()
a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
a = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("\033[?25l" )
sys.stdout.flush()
def UpperCAmelCase__ ( ):
'''simple docstring'''
if os.name == "nt":
a = CursorInfo()
a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
a = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("\033[?25h" )
sys.stdout.flush()
@contextmanager
def UpperCAmelCase__ ( ):
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 32
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Any = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''xglm'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {
'''num_attention_heads''': '''attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Dict , __lowerCAmelCase : Any=25_6008 , __lowerCAmelCase : Optional[int]=2048 , __lowerCAmelCase : Optional[Any]=1024 , __lowerCAmelCase : Optional[int]=4096 , __lowerCAmelCase : int=24 , __lowerCAmelCase : Any=16 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : List[str]=0.0_2 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : int=2 , __lowerCAmelCase : Any=1 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=2 , **__lowerCAmelCase : List[Any] , ) -> Any:
"""simple docstring"""
a = vocab_size
a = max_position_embeddings
a = d_model
a = ffn_dim
a = num_layers
a = attention_heads
a = activation_function
a = dropout
a = attention_dropout
a = activation_dropout
a = layerdrop
a = init_std
a = scale_embedding # scale factor will be sqrt(d_model) if True
a = use_cache
super().__init__(
pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , **__lowerCAmelCase , )
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
a = 0
a = str(UpperCAmelCase__ )
while len(UpperCAmelCase__ ) != 1:
a = [int(UpperCAmelCase__ ) for i in num_string]
a = 1
for i in range(0 , len(UpperCAmelCase__ ) ):
total *= numbers[i]
a = str(UpperCAmelCase__ )
steps += 1
return steps
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
a = 0
a = str(UpperCAmelCase__ )
while len(UpperCAmelCase__ ) != 1:
a = [int(UpperCAmelCase__ ) for i in num_string]
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
total += numbers[i]
a = str(UpperCAmelCase__ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 1
|
import json
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :Optional[int] ):
'''simple docstring'''
with open(UpperCAmelCase__ , encoding="utf-8" ) as f:
a = json.load(UpperCAmelCase__ )
a = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(UpperCAmelCase__ ):
a = results[benchmark_name]
a = benchmark_name.split("/" )[-1]
output_md.append(F"""### Benchmark: {benchmark_file_name}""" )
a = "| metric |"
a = "|--------|"
a = "| new / old (diff) |"
for metric_name in sorted(UpperCAmelCase__ ):
a = benchmark_res[metric_name]
a = metric_vals["new"]
a = metric_vals.get("old" , UpperCAmelCase__ )
a = metric_vals.get("diff" , UpperCAmelCase__ )
a = F""" {new_val:f}""" if isinstance(UpperCAmelCase__ , (int, float) ) else "None"
if old_val is not None:
val_str += F""" / {old_val:f}""" if isinstance(UpperCAmelCase__ , (int, float) ) else "None"
if dif_val is not None:
val_str += F""" ({dif_val:f})""" if isinstance(UpperCAmelCase__ , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append("</details>" )
with open(UpperCAmelCase__ , "w" , encoding="utf-8" ) as f:
f.writelines("\n".join(UpperCAmelCase__ ) )
if __name__ == "__main__":
A_ : Union[str, Any] = sys.argv[1]
A_ : Dict = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 32
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 1
|
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Dict ):
'''simple docstring'''
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
a = flax_key_tuple[:-1] + ("weight",)
a = torch.permute(UpperCAmelCase__ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCAmelCase__ ):
# linear layer
a = flax_key_tuple[:-1] + ("weight",)
a = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
a = flax_key_tuple[:-1] + ("weight",)
return flax_key_tuple, flax_tensor
def UpperCAmelCase__ ( UpperCAmelCase__ :List[Any] , UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :List[str] ):
'''simple docstring'''
if "metadata" in layer:
a = layer.split("metadata" )
a = "".join(split_layer[0] )[:-1]
a = [tuple(("metadata" + split_layer[1]).split("/" ) )]
elif "kvstore" in layer:
a = layer.split("kvstore" )
a = "".join(split_layer[0] )[:-1]
a = [tuple(("kvstore" + split_layer[1]).split("/" ) )]
else:
a = layer.split("/" )
a = "/".join(split_layer[:-1] )
a = (split_layer[-1],)
if "kvstore/path" in layer:
a = F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
elif "kvstore/driver" in layer:
a = "file"
else:
a = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Tuple ):
'''simple docstring'''
a = rename_keys(UpperCAmelCase__ )
a = {}
for k, v in current_block.items():
a = v
a = new_current_block
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] , UpperCAmelCase__ :Union[str, Any] , UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :Any , UpperCAmelCase__ :str = WEIGHTS_NAME ):
'''simple docstring'''
a = convert_file_size_to_int(UpperCAmelCase__ )
a = []
a = {}
a = 0
a = 0
os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
with gfile.GFile(switch_checkpoint_path + "/checkpoint" , "rb" ) as fp:
a = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
a = flatten_dict(UpperCAmelCase__ , sep="/" )
a = {}
for layer in checkpoint_info.keys():
a , a , a = get_key_and_tensorstore_dict(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if curr_real_layer_name in all_layers:
a = content
else:
a = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
a = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
a = torch.tensor(UpperCAmelCase__ )
a = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
a , a = rename_base_flax_keys(tuple(key.split("/" ) ) , UpperCAmelCase__ )
a = "/".join(UpperCAmelCase__ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
a = os.path.join(
UpperCAmelCase__ , weights_name.replace(".bin" , F"""-{len(UpperCAmelCase__ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(UpperCAmelCase__ , UpperCAmelCase__ )
sharded_state_dicts.append(current_block.keys() )
del current_block
a = {}
a = 0
a = raw_weights.to(getattr(UpperCAmelCase__ , UpperCAmelCase__ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
a = os.path.join(UpperCAmelCase__ , weights_name.replace(".bin" , F"""-{len(UpperCAmelCase__ )+1:05d}-of-???.bin""" ) )
rename_and_save_block(UpperCAmelCase__ , UpperCAmelCase__ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(UpperCAmelCase__ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
a = {}
a = {}
for idx, shard in enumerate(UpperCAmelCase__ ):
a = weights_name.replace(
".bin" , F"""-{idx+1:05d}-of-{len(UpperCAmelCase__ ):05d}.bin""" ) # len(sharded_state_dicts):05d}
a = os.path.join(UpperCAmelCase__ , weights_name.replace(".bin" , F"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
a = shard
for key in shard:
a = shard_file
# Add the metadata
a = {"total_size": total_size}
a = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , "w" , encoding="utf-8" ) as f:
a = json.dumps(UpperCAmelCase__ , indent=2 , sort_keys=UpperCAmelCase__ ) + "\n"
f.write(UpperCAmelCase__ )
return metadata, index
if __name__ == "__main__":
A_ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--switch_t5x_checkpoint_path''',
default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
type=str,
required=False,
help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
)
parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
type=str,
required=False,
help='''Path to the output pytorch model.''',
)
A_ : Dict = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def UpperCAmelCase__ ( ):
'''simple docstring'''
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
a = SwitchTransformersConfig.from_pretrained("google/switch-base-8" )
config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted" )
a = SwitchTransformersForConditionalGeneration.from_pretrained(
"/home/arthur_huggingface_co/transformers/switch_converted" , device_map="auto" )
a = TaTokenizer.from_pretrained("t5-small" )
a = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
a = tokenizer(UpperCAmelCase__ , return_tensors="pt" ).input_ids
a = model.generate(UpperCAmelCase__ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 32
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Optional[int] = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] = ['''MobileNetV2FeatureExtractor''']
A_ : Tuple = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 1
|
from collections.abc import Iterable
from typing import Any
class _lowercase :
def __init__( self : Optional[int] , __lowerCAmelCase : int | None = None ) -> Optional[Any]:
"""simple docstring"""
a = value
a = None # Added in order to delete a node easier
a = None
a = None
def __repr__( self : str ) -> str:
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f"""{self.value}""": (self.left, self.right)} , indent=1 )
class _lowercase :
def __init__( self : int , __lowerCAmelCase : Node | None = None ) -> str:
"""simple docstring"""
a = root
def __str__( self : str ) -> str:
"""simple docstring"""
return str(self.root )
def A ( self : Any , __lowerCAmelCase : Node , __lowerCAmelCase : Node | None ) -> None:
"""simple docstring"""
if new_children is not None: # reset its kids
a = node.parent
if node.parent is not None: # reset its parent
if self.is_right(__lowerCAmelCase ): # If it is the right children
a = new_children
else:
a = new_children
else:
a = new_children
def A ( self : Tuple , __lowerCAmelCase : Node ) -> bool:
"""simple docstring"""
if node.parent and node.parent.right:
return node == node.parent.right
return False
def A ( self : str ) -> bool:
"""simple docstring"""
return self.root is None
def A ( self : int , __lowerCAmelCase : List[Any] ) -> None:
"""simple docstring"""
a = Node(__lowerCAmelCase ) # create a new Node
if self.empty(): # if Tree is empty
a = new_node # set its root
else: # Tree is not empty
a = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
a = new_node # We insert the new node in a leaf
break
else:
a = parent_node.left
else:
if parent_node.right is None:
a = new_node
break
else:
a = parent_node.right
a = parent_node
def A ( self : Any , *__lowerCAmelCase : Any ) -> None:
"""simple docstring"""
for value in values:
self.__insert(__lowerCAmelCase )
def A ( self : Tuple , __lowerCAmelCase : List[str] ) -> Node | None:
"""simple docstring"""
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
a = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
a = node.left if value < node.value else node.right
return node
def A ( self : Dict , __lowerCAmelCase : Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
if self.root is None:
return None
a = self.root
if not self.empty():
while node.right is not None:
a = node.right
return node
def A ( self : Tuple , __lowerCAmelCase : Node | None = None ) -> Node | None:
"""simple docstring"""
if node is None:
a = self.root
if self.root is None:
return None
if not self.empty():
a = self.root
while node.left is not None:
a = node.left
return node
def A ( self : Any , __lowerCAmelCase : int ) -> None:
"""simple docstring"""
a = self.search(__lowerCAmelCase ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(__lowerCAmelCase , __lowerCAmelCase )
elif node.left is None: # Has only right children
self.__reassign_nodes(__lowerCAmelCase , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(__lowerCAmelCase , node.left )
else:
a = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
a = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def A ( self : Any , __lowerCAmelCase : Node | None ) -> Iterable:
"""simple docstring"""
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def A ( self : Dict , __lowerCAmelCase : Tuple=None ) -> Any:
"""simple docstring"""
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def A ( self : str , __lowerCAmelCase : list , __lowerCAmelCase : Node | None ) -> None:
"""simple docstring"""
if node:
self.inorder(__lowerCAmelCase , node.left )
arr.append(node.value )
self.inorder(__lowerCAmelCase , node.right )
def A ( self : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Node ) -> int:
"""simple docstring"""
a = []
self.inorder(__lowerCAmelCase , __lowerCAmelCase ) # append all values to list using inorder traversal
return arr[k - 1]
def UpperCAmelCase__ ( UpperCAmelCase__ :Node | None ):
'''simple docstring'''
a = []
if curr_node is not None:
a = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = (8, 3, 6, 1, 10, 14, 13, 4, 7)
a = BinarySearchTree()
for i in testlist:
t.insert(UpperCAmelCase__ )
# Prints all the elements of the list in order traversal
print(UpperCAmelCase__ )
if t.search(6 ) is not None:
print("The value 6 exists" )
else:
print("The value 6 doesn't exist" )
if t.search(-1 ) is not None:
print("The value -1 exists" )
else:
print("The value -1 doesn't exist" )
if not t.empty():
print("Max Value: " , t.get_max().value ) # type: ignore
print("Min Value: " , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(UpperCAmelCase__ )
print(UpperCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 32
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a , a = 1, 1
a = []
for i in range(1 , n + 1 ):
a = prev_numerator + 2 * prev_denominator
a = prev_numerator + prev_denominator
if len(str(UpperCAmelCase__ ) ) > len(str(UpperCAmelCase__ ) ):
result.append(UpperCAmelCase__ )
a = numerator
a = denominator
return len(UpperCAmelCase__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
|
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 _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
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=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , 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] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
| 1
|
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = None
_UpperCAmelCase = 1
_UpperCAmelCase = None
_UpperCAmelCase = False
_UpperCAmelCase = None
_UpperCAmelCase = None
def A ( self : Any ) -> "DownloadConfig":
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(__lowerCAmelCase ) for k, v in self.__dict__.items()} )
| 32
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 1
|
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
A_ : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name
A_ : Dict = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] , UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :Optional[int]=8 ):
'''simple docstring'''
a = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
a = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDConditionModel , __lowerCAmelCase : DDPMScheduler , __lowerCAmelCase : VQModel , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
self.register_modules(
unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , movq=__lowerCAmelCase , )
a = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A ( self : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
if latents is None:
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase , device=__lowerCAmelCase , dtype=__lowerCAmelCase )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
a = latents.to(__lowerCAmelCase )
a = latents * scheduler.init_noise_sigma
return latents
def A ( self : Any , __lowerCAmelCase : Any=0 ) -> List[Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
a = torch.device(f"""cuda:{gpu_id}""" )
a = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : Dict , __lowerCAmelCase : Any=0 ) -> Any:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
a = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=__lowerCAmelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
a = None
for cpu_offloaded_model in [self.unet, self.movq]:
a , a = cpu_offload_with_hook(__lowerCAmelCase , __lowerCAmelCase , prev_module_hook=__lowerCAmelCase )
# We'll offload the last model manually.
a = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A ( self : List[Any] ) -> str:
"""simple docstring"""
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__lowerCAmelCase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__lowerCAmelCase )
def __call__( self : List[str] , __lowerCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __lowerCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]] , __lowerCAmelCase : int = 512 , __lowerCAmelCase : int = 512 , __lowerCAmelCase : int = 100 , __lowerCAmelCase : float = 4.0 , __lowerCAmelCase : int = 1 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[torch.FloatTensor] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , ) -> List[str]:
"""simple docstring"""
a = self._execution_device
a = guidance_scale > 1.0
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = torch.cat(__lowerCAmelCase , dim=0 )
a = image_embeds.shape[0] * num_images_per_prompt
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = torch.cat(__lowerCAmelCase , dim=0 )
if do_classifier_free_guidance:
a = image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 )
a = negative_image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 )
a = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__lowerCAmelCase )
self.scheduler.set_timesteps(__lowerCAmelCase , device=__lowerCAmelCase )
a = self.scheduler.timesteps
a = self.unet.config.in_channels
a , a = downscale_height_and_width(__lowerCAmelCase , __lowerCAmelCase , self.movq_scale_factor )
# create initial latent
a = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__lowerCAmelCase ) ):
# expand the latents if we are doing classifier free guidance
a = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
a = {"image_embeds": image_embeds}
a = self.unet(
sample=__lowerCAmelCase , timestep=__lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase , added_cond_kwargs=__lowerCAmelCase , return_dict=__lowerCAmelCase , )[0]
if do_classifier_free_guidance:
a , a = noise_pred.split(latents.shape[1] , dim=1 )
a , a = noise_pred.chunk(2 )
a , a = variance_pred.chunk(2 )
a = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
a = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
a , a = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
a = self.scheduler.step(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase , )[0]
# post-processing
a = self.movq.decode(__lowerCAmelCase , force_not_quantize=__lowerCAmelCase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
a = image * 0.5 + 0.5
a = image.clamp(0 , 1 )
a = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Dict = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = [
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''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()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 1
|
# Function to print upper half of diamond (pyramid)
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
for i in range(0 , UpperCAmelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , end="" )
print()
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
for i in range(UpperCAmelCase__ , 0 , -1 ):
for _ in range(UpperCAmelCase__ , 0 , -1 ): # printing stars
print("* " , end="" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(" " , end="" )
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] ):
'''simple docstring'''
if n <= 0:
print(" ... .... nothing printing :(" )
return
floyd(UpperCAmelCase__ ) # upper half
reverse_floyd(UpperCAmelCase__ ) # lower half
if __name__ == "__main__":
print(r'''| /\ | |- | |- |--| |\ /| |-''')
print(r'''|/ \| |- |_ |_ |__| | \/ | |_''')
A_ : Optional[Any] = 1
while K:
A_ : int = int(input('''enter the number and , and see the magic : '''))
print()
pretty_print(user_number)
A_ : Optional[Any] = int(input('''press 0 to exit... and 1 to continue...'''))
print('''Good Bye...''')
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 1
|
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
A_ : Dict = logging.getLogger()
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = argparse.ArgumentParser()
parser.add_argument("-f" )
a = parser.parse_args()
return args.f
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] ):
'''simple docstring'''
a = {}
a = os.path.join(UpperCAmelCase__ , "all_results.json" )
if os.path.exists(UpperCAmelCase__ ):
with open(UpperCAmelCase__ , "r" ) as f:
a = json.load(UpperCAmelCase__ )
else:
raise ValueError(F"""can't find {path}""" )
return results
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = torch.cuda.is_available() and torch_device == "cuda"
return is_using_cuda and is_apex_available()
A_ : Tuple = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _lowercase ( UpperCAmelCase__ ):
@classmethod
def A ( cls : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a = tempfile.mkdtemp()
a = os.path.join(cls.tmpdir , "default_config.yml" )
write_basic_config(save_location=cls.configPath )
a = ["accelerate", "launch", "--config_file", cls.configPath]
@classmethod
def A ( cls : List[Any] ) -> Optional[Any]:
"""simple docstring"""
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "glue_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[str] ) -> Tuple:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertLess(result["perplexity"] , 100 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "clm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertLess(result["perplexity"] , 42 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "mlm_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
a = 7 if get_gpu_count() > 1 else 2
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
self.assertLess(result["train_loss"] , 0.5 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "ner_no_trainer" ) ) )
@unittest.skip(reason="Fix me @muellerzr" )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 28 )
self.assertGreaterEqual(result["eval_exact"] , 28 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "qa_no_trainer" ) ) )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.8 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "swag_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_rouge1"] , 10 )
self.assertGreaterEqual(result["eval_rouge2"] , 2 )
self.assertGreaterEqual(result["eval_rougeL"] , 7 )
self.assertGreaterEqual(result["eval_rougeLsum"] , 7 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "summarization_no_trainer" ) ) )
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_bleu"] , 30 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "epoch_0" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "translation_no_trainer" ) ) )
@slow
def A ( self : Tuple ) -> Tuple:
"""simple docstring"""
a = logging.StreamHandler(sys.stdout )
logger.addHandler(__lowerCAmelCase )
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.1_0 )
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"} )
def A ( self : List[Any] ) -> List[str]:
"""simple docstring"""
a = self.get_auto_remove_tmp_dir()
a = f"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16" )
run_command(self._launch_args + testargs )
a = get_results(__lowerCAmelCase )
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6 )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "step_1" ) ) )
self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase , "image_classification_no_trainer" ) ) )
| 32
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 1
|
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
A_ : List[str] = 2_99_79_24_58
# Symbols
A_ , A_ , A_ , A_ : Union[str, Any] = symbols('''ct x y z''')
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
if velocity > c:
raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("Speed must be greater than or equal to 1!" )
return velocity / c
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
return 1 / sqrt(1 - beta(UpperCAmelCase__ ) ** 2 )
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
return np.array(
[
[gamma(UpperCAmelCase__ ), -gamma(UpperCAmelCase__ ) * beta(UpperCAmelCase__ ), 0, 0],
[-gamma(UpperCAmelCase__ ) * beta(UpperCAmelCase__ ), gamma(UpperCAmelCase__ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :np.ndarray | None = None ):
'''simple docstring'''
if event is None:
a = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(UpperCAmelCase__ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
A_ : Tuple = transform(29_97_92_45)
print('''Example of four vector: ''')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
A_ : List[str] = {ct: c, x: 1, y: 1, z: 1}
A_ : List[str] = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 32
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 1
|
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
A_ : List[str] = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_UpperCAmelCase = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_UpperCAmelCase = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def A ( self : str , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Tuple ) -> Any:
"""simple docstring"""
a = ZeroShotClassificationPipeline(
model=__lowerCAmelCase , tokenizer=__lowerCAmelCase , candidate_labels=["polics", "health"] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def A ( self : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
a = classifier("Who are you voting for in 2020?" , candidate_labels="politics" )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
# No kwarg
a = classifier("Who are you voting for in 2020?" , ["politics"] )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
a = classifier("Who are you voting for in 2020?" , candidate_labels=["politics"] )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
a = classifier("Who are you voting for in 2020?" , candidate_labels="politics, public health" )
self.assertEqual(
__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
a = classifier("Who are you voting for in 2020?" , candidate_labels=["politics", "public health"] )
self.assertEqual(
__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["scores"] ) ) , 1.0 )
a = classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="This text is about {}" )
self.assertEqual(__lowerCAmelCase , {"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
a = classifier(["I am happy"] , ["positive", "negative"] )
self.assertEqual(
__lowerCAmelCase , [
{"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]}
for i in range(1 )
] , )
a = classifier(["I am happy", "I am sad"] , ["positive", "negative"] )
self.assertEqual(
__lowerCAmelCase , [
{"sequence": ANY(__lowerCAmelCase ), "labels": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )], "scores": [ANY(__lowerCAmelCase ), ANY(__lowerCAmelCase )]}
for i in range(2 )
] , )
with self.assertRaises(__lowerCAmelCase ):
classifier("" , candidate_labels="politics" )
with self.assertRaises(__lowerCAmelCase ):
classifier(__lowerCAmelCase , candidate_labels="politics" )
with self.assertRaises(__lowerCAmelCase ):
classifier("Who are you voting for in 2020?" , candidate_labels="" )
with self.assertRaises(__lowerCAmelCase ):
classifier("Who are you voting for in 2020?" , candidate_labels=__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template="Not formatting template" , )
with self.assertRaises(__lowerCAmelCase ):
classifier(
"Who are you voting for in 2020?" , candidate_labels="politics" , hypothesis_template=__lowerCAmelCase , )
self.run_entailment_id(__lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : Pipeline ) -> Dict:
"""simple docstring"""
a = zero_shot_classifier.model.config
a = config.labelaid
a = zero_shot_classifier.entailment_id
a = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
a = {"entailment": 0, "neutral": 1, "contradiction": 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
a = {"ENTAIL": 0, "NON-ENTAIL": 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
a = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
a = original_labelaid
self.assertEqual(__lowerCAmelCase , zero_shot_classifier.entailment_id )
@require_torch
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"Who are you voting for in 2020?" * 100 , candidate_labels=["politics", "public health", "science"] )
@require_torch
def A ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="pt" , )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@require_tf
def A ( self : int ) -> Union[str, Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-classification" , model="sshleifer/tiny-distilbert-base-cased-distilled-squad" , framework="tf" , )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["science", "public health", "politics"],
"scores": [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@slow
@require_torch
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
a = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="pt" )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
a = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=__lowerCAmelCase , )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
@slow
@require_tf
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = pipeline("zero-shot-classification" , model="roberta-large-mnli" , framework="tf" )
a = zero_shot_classifier(
"Who are you voting for in 2020?" , candidate_labels=["politics", "public health", "science"] )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": "Who are you voting for in 2020?",
"labels": ["politics", "public health", "science"],
"scores": [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
a = zero_shot_classifier(
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
" machine translation tasks show these models to be superior in quality while being more parallelizable"
" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
" English-to-German translation task, improving over the existing best results, including ensembles by"
" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
" fraction of the training costs of the best models from the literature. We show that the Transformer"
" generalizes well to other tasks by applying it successfully to English constituency parsing both with"
" large and limited training data." , candidate_labels=["machine learning", "statistics", "translation", "vision"] , multi_label=__lowerCAmelCase , )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
"sequence": (
"The dominant sequence transduction models are based on complex recurrent or convolutional neural"
" networks in an encoder-decoder configuration. The best performing models also connect the"
" encoder and decoder through an attention mechanism. We propose a new simple network"
" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
" and convolutions entirely. Experiments on two machine translation tasks show these models to be"
" superior in quality while being more parallelizable and requiring significantly less time to"
" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
" English-to-French translation task, our model establishes a new single-model state-of-the-art"
" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
" costs of the best models from the literature. We show that the Transformer generalizes well to"
" other tasks by applying it successfully to English constituency parsing both with large and"
" limited training data."
),
"labels": ["translation", "machine learning", "vision", "statistics"],
"scores": [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
| 32
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 1
|
from typing import List
from .keymap import KEYMAP, get_character
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
def decorator(UpperCAmelCase__ :Any ):
a = getattr(UpperCAmelCase__ , "handle_key" , [] )
handle += [key]
setattr(UpperCAmelCase__ , "handle_key" , UpperCAmelCase__ )
return func
return decorator
def UpperCAmelCase__ ( *UpperCAmelCase__ :List[str] ):
'''simple docstring'''
def decorator(UpperCAmelCase__ :str ):
a = getattr(UpperCAmelCase__ , "handle_key" , [] )
handle += keys
setattr(UpperCAmelCase__ , "handle_key" , UpperCAmelCase__ )
return func
return decorator
class _lowercase ( UpperCAmelCase__ ):
def __new__( cls : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
a = super().__new__(cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not hasattr(__lowerCAmelCase , "key_handler" ):
setattr(__lowerCAmelCase , "key_handler" , {} )
setattr(__lowerCAmelCase , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
a = getattr(__lowerCAmelCase , "handle_key" , [] )
for key in handled_keys:
a = value
return new_cls
@staticmethod
def A ( cls : int ) -> Optional[Any]:
"""simple docstring"""
a = get_character()
if char != KEYMAP["undefined"]:
a = ord(__lowerCAmelCase )
a = cls.key_handler.get(__lowerCAmelCase )
if handler:
a = char
return handler(cls )
else:
return None
def UpperCAmelCase__ ( cls :Tuple ):
'''simple docstring'''
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 32
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :list ):
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
a = grid[0]
for row_n in range(1 , len(UpperCAmelCase__ ) ):
a = grid[row_n]
a = fill_row(UpperCAmelCase__ , UpperCAmelCase__ )
a = grid[row_n]
return grid[-1][-1]
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :list ):
'''simple docstring'''
current_row[0] += row_above[0]
for cell_n in range(1 , len(UpperCAmelCase__ ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 1
|
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
A_ : Any = logging.getLogger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''summarization'''
_UpperCAmelCase = ['''loss''']
_UpperCAmelCase = ROUGE_KEYS
_UpperCAmelCase = '''rouge2'''
def __init__( self : int , __lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
if hparams.sortish_sampler and hparams.gpus > 1:
a = 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__(__lowerCAmelCase , num_labels=__lowerCAmelCase , mode=self.mode , **__lowerCAmelCase )
use_task_specific_params(self.model , "summarization" )
save_git_info(self.hparams.output_dir )
a = Path(self.output_dir ) / "metrics.json"
a = Path(self.output_dir ) / "hparams.pkl"
pickle_save(self.hparams , self.hparams_save_path )
a = 0
a = defaultdict(__lowerCAmelCase )
a = self.config.model_type
a = self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size
a = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
a = {
"train": self.hparams.n_train,
"val": self.hparams.n_val,
"test": self.hparams.n_test,
}
a = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
a = {
"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() )
a = get_git_info()["repo_sha"]
a = hparams.num_workers
a = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , __lowerCAmelCase ):
a = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
a = self.decoder_start_token_id
a = (
SeqaSeqDataset if hasattr(self.tokenizer , "prepare_seq2seq_batch" ) else LegacySeqaSeqDataset
)
a = False
a = 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:
a = self.hparams.eval_max_gen_length
else:
a = self.model.config.max_length
a = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def A ( self : Tuple , __lowerCAmelCase : Dict[str, torch.Tensor] ) -> Dict[str, List[str]]:
"""simple docstring"""
a = {
k: self.tokenizer.batch_decode(v.tolist() ) if "mask" not in k else v.shape for k, v in batch.items()
}
save_json(__lowerCAmelCase , 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" )
a = True
return readable_batch
def A ( self : str , __lowerCAmelCase : List[str] , **__lowerCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
return self.model(__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : Dict , __lowerCAmelCase : List[int] ) -> Optional[Any]:
"""simple docstring"""
a = self.tokenizer.batch_decode(
__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
return lmap(str.strip , __lowerCAmelCase )
def A ( self : int , __lowerCAmelCase : dict ) -> Tuple:
"""simple docstring"""
a = self.tokenizer.pad_token_id
a , a = batch["input_ids"], batch["attention_mask"]
a = batch["labels"]
if isinstance(self.model , __lowerCAmelCase ):
a = self.model._shift_right(__lowerCAmelCase )
else:
a = shift_tokens_right(__lowerCAmelCase , __lowerCAmelCase )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
a = decoder_input_ids
self.save_readable_batch(__lowerCAmelCase )
a = self(__lowerCAmelCase , attention_mask=__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase , use_cache=__lowerCAmelCase )
a = outputs["logits"]
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
a = nn.CrossEntropyLoss(ignore_index=__lowerCAmelCase )
assert lm_logits.shape[-1] == self.vocab_size
a = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
a = nn.functional.log_softmax(__lowerCAmelCase , dim=-1 )
a , a = label_smoothed_nll_loss(
__lowerCAmelCase , __lowerCAmelCase , self.hparams.label_smoothing , ignore_index=__lowerCAmelCase )
return (loss,)
@property
def A ( self : str ) -> int:
"""simple docstring"""
return self.tokenizer.pad_token_id
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
a = self._step(__lowerCAmelCase )
a = dict(zip(self.loss_names , __lowerCAmelCase ) )
# tokens per batch
a = batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
a = batch["input_ids"].shape[0]
a = batch["input_ids"].eq(self.pad ).sum()
a = 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 A ( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
return self._generative_step(__lowerCAmelCase )
def A ( self : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str="val" ) -> Dict:
"""simple docstring"""
self.step_count += 1
a = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
a = losses["loss"]
a = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ["gen_time", "gen_len"]
}
a = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
a = torch.tensor(__lowerCAmelCase ).type_as(__lowerCAmelCase )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(__lowerCAmelCase )
a = {f"""{prefix}_avg_{k}""": x for k, x in losses.items()}
a = self.step_count
self.metrics[prefix].append(__lowerCAmelCase ) # callback writes this to self.metrics_save_path
a = 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 A ( self : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
return calculate_rouge(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : dict ) -> dict:
"""simple docstring"""
a = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
a = self.model.generate(
batch["input_ids"] , attention_mask=batch["attention_mask"] , use_cache=__lowerCAmelCase , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
a = (time.time() - ta) / batch["input_ids"].shape[0]
a = self.ids_to_clean_text(__lowerCAmelCase )
a = self.ids_to_clean_text(batch["labels"] )
a = self._step(__lowerCAmelCase )
a = dict(zip(self.loss_names , __lowerCAmelCase ) )
a = self.calc_generative_metrics(__lowerCAmelCase , __lowerCAmelCase )
a = np.mean(lmap(__lowerCAmelCase , __lowerCAmelCase ) )
base_metrics.update(gen_time=__lowerCAmelCase , gen_len=__lowerCAmelCase , preds=__lowerCAmelCase , target=__lowerCAmelCase , **__lowerCAmelCase )
return base_metrics
def A ( self : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
return self._generative_step(__lowerCAmelCase )
def A ( self : str , __lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return self.validation_epoch_end(__lowerCAmelCase , prefix="test" )
def A ( self : int , __lowerCAmelCase : List[Any] ) -> SeqaSeqDataset:
"""simple docstring"""
a = self.n_obs[type_path]
a = self.target_lens[type_path]
a = self.dataset_class(
self.tokenizer , type_path=__lowerCAmelCase , n_obs=__lowerCAmelCase , max_target_length=__lowerCAmelCase , **self.dataset_kwargs , )
return dataset
def A ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : bool = False ) -> DataLoader:
"""simple docstring"""
a = self.get_dataset(__lowerCAmelCase )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
a = dataset.make_sortish_sampler(__lowerCAmelCase , distributed=self.hparams.gpus > 1 )
return DataLoader(
__lowerCAmelCase , batch_size=__lowerCAmelCase , collate_fn=dataset.collate_fn , shuffle=__lowerCAmelCase , num_workers=self.num_workers , sampler=__lowerCAmelCase , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
a = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
__lowerCAmelCase , batch_sampler=__lowerCAmelCase , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
__lowerCAmelCase , batch_size=__lowerCAmelCase , collate_fn=dataset.collate_fn , shuffle=__lowerCAmelCase , num_workers=self.num_workers , sampler=__lowerCAmelCase , )
def A ( self : Union[str, Any] ) -> DataLoader:
"""simple docstring"""
a = self.get_dataloader("train" , batch_size=self.hparams.train_batch_size , shuffle=__lowerCAmelCase )
return dataloader
def A ( self : Any ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("val" , batch_size=self.hparams.eval_batch_size )
def A ( self : List[Any] ) -> DataLoader:
"""simple docstring"""
return self.get_dataloader("test" , batch_size=self.hparams.eval_batch_size )
@staticmethod
def A ( __lowerCAmelCase : str , __lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
BaseTransformer.add_model_specific_args(__lowerCAmelCase , __lowerCAmelCase )
add_generic_args(__lowerCAmelCase , __lowerCAmelCase )
parser.add_argument(
"--max_source_length" , default=1024 , type=__lowerCAmelCase , 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=__lowerCAmelCase , 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=142 , type=__lowerCAmelCase , 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=142 , type=__lowerCAmelCase , 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=__lowerCAmelCase )
parser.add_argument("--overwrite_output_dir" , action="store_true" , default=__lowerCAmelCase )
parser.add_argument("--max_tokens_per_batch" , type=__lowerCAmelCase , default=__lowerCAmelCase )
parser.add_argument("--logger_name" , type=__lowerCAmelCase , choices=["default", "wandb", "wandb_shared"] , default="default" )
parser.add_argument("--n_train" , type=__lowerCAmelCase , default=-1 , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument("--n_val" , type=__lowerCAmelCase , default=500 , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument("--n_test" , type=__lowerCAmelCase , default=-1 , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument(
"--task" , type=__lowerCAmelCase , default="summarization" , required=__lowerCAmelCase , help="# examples. -1 means use all." )
parser.add_argument("--label_smoothing" , type=__lowerCAmelCase , default=0.0 , required=__lowerCAmelCase )
parser.add_argument("--src_lang" , type=__lowerCAmelCase , default="" , required=__lowerCAmelCase )
parser.add_argument("--tgt_lang" , type=__lowerCAmelCase , default="" , required=__lowerCAmelCase )
parser.add_argument("--eval_beams" , type=__lowerCAmelCase , default=__lowerCAmelCase , required=__lowerCAmelCase )
parser.add_argument(
"--val_metric" , type=__lowerCAmelCase , default=__lowerCAmelCase , required=__lowerCAmelCase , choices=["bleu", "rouge2", "loss", None] )
parser.add_argument("--eval_max_gen_length" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="never generate more than n tokens" )
parser.add_argument("--save_top_k" , type=__lowerCAmelCase , default=1 , required=__lowerCAmelCase , help="How many checkpoints to save" )
parser.add_argument(
"--early_stopping_patience" , type=__lowerCAmelCase , default=-1 , required=__lowerCAmelCase , 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 _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''translation'''
_UpperCAmelCase = ['''loss''']
_UpperCAmelCase = ['''bleu''']
_UpperCAmelCase = '''bleu'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : str , **__lowerCAmelCase : Tuple ) -> List[str]:
"""simple docstring"""
super().__init__(__lowerCAmelCase , **__lowerCAmelCase )
a = hparams.src_lang
a = hparams.tgt_lang
def A ( self : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> dict:
"""simple docstring"""
return calculate_bleu(__lowerCAmelCase , __lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :int=None ):
'''simple docstring'''
Path(args.output_dir ).mkdir(exist_ok=UpperCAmelCase__ )
check_output_dir(UpperCAmelCase__ , expected_items=3 )
if model is None:
if "summarization" in args.task:
a = SummarizationModule(UpperCAmelCase__ )
else:
a = TranslationModule(UpperCAmelCase__ )
a = 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" )
):
a = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
a = os.environ.get("WANDB_PROJECT" , UpperCAmelCase__ )
a = WandbLogger(name=model.output_dir.name , project=UpperCAmelCase__ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
a = WandbLogger(name=model.output_dir.name , project=F"""hf_{dataset}""" )
if args.early_stopping_patience >= 0:
a = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
a = False
a = args.val_metric == "loss"
a = generic_train(
UpperCAmelCase__ , UpperCAmelCase__ , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , UpperCAmelCase__ ) , early_stopping_callback=UpperCAmelCase__ , logger=UpperCAmelCase__ , )
pickle_save(model.hparams , model.output_dir / "hparams.pkl" )
if not args.do_predict:
return model
a = ""
a = sorted(glob.glob(os.path.join(args.output_dir , "*.ckpt" ) , recursive=UpperCAmelCase__ ) )
if checkpoints:
a = checkpoints[-1]
a = 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__":
A_ : str = argparse.ArgumentParser()
A_ : Union[str, Any] = pl.Trainer.add_argparse_args(parser)
A_ : Tuple = SummarizationModule.add_model_specific_args(parser, os.getcwd())
A_ : Any = parser.parse_args()
main(args)
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 1
|
import warnings
from functools import wraps
from typing import Callable
def UpperCAmelCase__ ( UpperCAmelCase__ :Callable ):
'''simple docstring'''
@wraps(UpperCAmelCase__ )
def _inner_fn(*UpperCAmelCase__ :List[str] , **UpperCAmelCase__ :str ):
warnings.warn(
(F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""") , UpperCAmelCase__ , )
return fn(*UpperCAmelCase__ , **UpperCAmelCase__ )
return _inner_fn
| 32
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 1
|
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
A_ : Union[str, Any] = '''src/diffusers'''
A_ : List[Any] = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
A_ : List[str] = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
A_ : Optional[int] = spec.loader.load_module()
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
return line.startswith(UpperCAmelCase__ ) or len(UpperCAmelCase__ ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$" , UpperCAmelCase__ ) is not None
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
a = object_name.split("." )
a = 0
# First let's find the module where our object lives.
a = parts[i]
while i < len(UpperCAmelCase__ ) and not os.path.isfile(os.path.join(UpperCAmelCase__ , F"""{module}.py""" ) ):
i += 1
if i < len(UpperCAmelCase__ ):
a = os.path.join(UpperCAmelCase__ , parts[i] )
if i >= len(UpperCAmelCase__ ):
raise ValueError(F"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(UpperCAmelCase__ , F"""{module}.py""" ) , "r" , encoding="utf-8" , newline="\n" ) as f:
a = f.readlines()
# Now let's find the class / func in the code!
a = ""
a = 0
for name in parts[i + 1 :]:
while (
line_index < len(UpperCAmelCase__ ) 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(UpperCAmelCase__ ):
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).
a = line_index
while line_index < len(UpperCAmelCase__ ) and _should_continue(lines[line_index] , UpperCAmelCase__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
a = lines[start_index:line_index]
return "".join(UpperCAmelCase__ )
A_ : List[Any] = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
A_ : List[Any] = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
A_ : int = re.compile(r'''<FILL\s+[^>]*>''')
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
a = code.split("\n" )
a = 0
while idx < len(UpperCAmelCase__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(UpperCAmelCase__ ):
return re.search(r"^(\s*)\S" , lines[idx] ).groups()[0]
return ""
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] ):
'''simple docstring'''
a = len(get_indent(UpperCAmelCase__ ) ) > 0
if has_indent:
a = F"""class Bla:\n{code}"""
a = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 , preview=UpperCAmelCase__ )
a = black.format_str(UpperCAmelCase__ , mode=UpperCAmelCase__ )
a , a = style_docstrings_in_code(UpperCAmelCase__ )
return result[len("class Bla:\n" ) :] if has_indent else result
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :str=False ):
'''simple docstring'''
with open(UpperCAmelCase__ , "r" , encoding="utf-8" , newline="\n" ) as f:
a = f.readlines()
a = []
a = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(UpperCAmelCase__ ):
a = _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.
a , a , a = search.groups()
a = find_code_in_diffusers(UpperCAmelCase__ )
a = get_indent(UpperCAmelCase__ )
a = line_index + 1 if indent == theoretical_indent else line_index + 2
a = theoretical_indent
a = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
a = True
while line_index < len(UpperCAmelCase__ ) and should_continue:
line_index += 1
if line_index >= len(UpperCAmelCase__ ):
break
a = lines[line_index]
a = _should_continue(UpperCAmelCase__ , UpperCAmelCase__ ) and re.search(F"""^{indent}# End copy""" , UpperCAmelCase__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
a = lines[start_index:line_index]
a = "".join(UpperCAmelCase__ )
# Remove any nested `Copied from` comments to avoid circular copies
a = [line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(UpperCAmelCase__ ) is None]
a = "\n".join(UpperCAmelCase__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(UpperCAmelCase__ ) > 0:
a = replace_pattern.replace("with" , "" ).split("," )
a = [_re_replace_pattern.search(UpperCAmelCase__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
a , a , a = pattern.groups()
a = re.sub(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if option.strip() == "all-casing":
a = re.sub(obja.lower() , obja.lower() , UpperCAmelCase__ )
a = re.sub(obja.upper() , obja.upper() , UpperCAmelCase__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
a = blackify(lines[start_index - 1] + theoretical_code )
a = 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:
a = lines[:start_index] + [theoretical_code] + lines[line_index:]
a = start_index + 1
if overwrite and len(UpperCAmelCase__ ) > 0:
# Warn the user a file has been modified.
print(F"""Detected changes, rewriting {filename}.""" )
with open(UpperCAmelCase__ , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(UpperCAmelCase__ )
return diffs
def UpperCAmelCase__ ( UpperCAmelCase__ :bool = False ):
'''simple docstring'''
a = glob.glob(os.path.join(UpperCAmelCase__ , "**/*.py" ) , recursive=UpperCAmelCase__ )
a = []
for filename in all_files:
a = is_copy_consistent(UpperCAmelCase__ , UpperCAmelCase__ )
diffs += [F"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(UpperCAmelCase__ ) > 0:
a = "\n".join(UpperCAmelCase__ )
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__":
A_ : List[Any] = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A_ : int = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 10_00 ):
'''simple docstring'''
a , a = 1, 1
a = 2
while True:
a = 0
a = fa + fa
a , a = fa, f
index += 1
for _ in str(UpperCAmelCase__ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 32
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 1
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A_ : Optional[Any] = 16
A_ : str = 32
def UpperCAmelCase__ ( UpperCAmelCase__ :Accelerator , UpperCAmelCase__ :int = 16 ):
'''simple docstring'''
a = AutoTokenizer.from_pretrained("bert-base-cased" )
a = load_dataset("glue" , "mrpc" )
def tokenize_function(UpperCAmelCase__ :str ):
# 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(UpperCAmelCase__ :List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
a = 1_28 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_ : str = mocked_dataloaders # noqa: F811
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple , UpperCAmelCase__ :int ):
'''simple docstring'''
if os.environ.get("TESTING_MOCKED_DATALOADERS" , UpperCAmelCase__ ) == "1":
a = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
a = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
a = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# 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"] )
set_seed(UpperCAmelCase__ )
a , a = get_dataloaders(UpperCAmelCase__ , UpperCAmelCase__ )
a = evaluate.load("glue" , "mrpc" )
# If the batch size is too big we use gradient accumulation
a = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
a = batch_size // MAX_GPU_BATCH_SIZE
a = MAX_GPU_BATCH_SIZE
# 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=1_00 , num_training_steps=(len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
a , a , a , a , a = accelerator.prepare(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
a = os.path.split(UpperCAmelCase__ )[-1].split("." )[0]
accelerator.init_trackers(UpperCAmelCase__ , UpperCAmelCase__ )
# Now we train the model
for epoch in range(UpperCAmelCase__ ):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
a = 0
for step, batch in enumerate(UpperCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
a = model(**UpperCAmelCase__ )
a = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
a = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase__ )
if step % gradient_accumulation_steps == 0:
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` (the default).
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__ )
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(UpperCAmelCase__ ),
"epoch": epoch,
} , step=UpperCAmelCase__ , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def UpperCAmelCase__ ( ):
'''simple docstring'''
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." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=UpperCAmelCase__ , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
a = parser.parse_args()
a = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 32
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 1
|
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A_ : int = '''▁'''
A_ : str = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class _lowercase ( UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = BigBirdTokenizer
_UpperCAmelCase = BigBirdTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
super().setUp()
a = self.tokenizer_class(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = "<s>"
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase ) , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "[MASK]" )
self.assertEqual(len(__lowerCAmelCase ) , 1004 )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
a = self.get_tokenizer()
a = self.get_rust_tokenizer()
a = "I was born in 92000, and this is falsé."
a = tokenizer.tokenize(__lowerCAmelCase )
a = rust_tokenizer.tokenize(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
a = tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
a = rust_tokenizer.encode(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
a = self.get_rust_tokenizer()
a = tokenizer.encode(__lowerCAmelCase )
a = rust_tokenizer.encode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : int ) -> Optional[Any]:
"""simple docstring"""
a = BigBirdTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
a = tokenizer.tokenize("This is a test" )
self.assertListEqual(__lowerCAmelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , [285, 46, 10, 170, 382] , )
a = 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",
".",
] , )
a = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
a = tokenizer.convert_ids_to_tokens(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
@cached_property
def A ( self : List[Any] ) -> Tuple:
"""simple docstring"""
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
@slow
def A ( self : int ) -> Any:
"""simple docstring"""
a = "Hello World!"
a = [65, 1_8536, 2260, 101, 66]
self.assertListEqual(__lowerCAmelCase , self.big_tokenizer.encode(__lowerCAmelCase ) )
@slow
def A ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
a = (
"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"
)
# fmt: off
a = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66] # noqa: E231
# fmt: on
self.assertListEqual(__lowerCAmelCase , self.big_tokenizer.encode(__lowerCAmelCase ) )
@require_torch
@slow
def A ( self : Dict ) -> Tuple:
"""simple docstring"""
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
a = list(self.big_tokenizer.get_vocab().keys() )[:10]
a = " ".join(__lowerCAmelCase )
a = self.big_tokenizer.encode_plus(__lowerCAmelCase , return_tensors="pt" , return_token_type_ids=__lowerCAmelCase )
a = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence] , return_tensors="pt" , return_token_type_ids=__lowerCAmelCase )
a = BigBirdConfig(attention_type="original_full" )
a = BigBirdModel(__lowerCAmelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__lowerCAmelCase )
model(**__lowerCAmelCase )
@slow
def A ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
a = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
a = tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )
@slow
def A ( self : int ) -> str:
"""simple docstring"""
a = {"input_ids": [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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="google/bigbird-roberta-base" , revision="215c99f1600e06f83acce68422f2035b2b5c3510" , )
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 1
|
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
def __init__( self : str , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : List[Any]=3 , __lowerCAmelCase : int=16 , __lowerCAmelCase : Dict=[32, 64, 128] , __lowerCAmelCase : str=[1, 2, 1] , __lowerCAmelCase : Optional[int]=[2, 2, 4] , __lowerCAmelCase : Optional[Any]=2 , __lowerCAmelCase : Dict=2.0 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : str=0.0 , __lowerCAmelCase : Dict=0.0 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : List[Any]=False , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Union[str, Any]=0.0_2 , __lowerCAmelCase : Tuple=1E-5 , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Dict=10 , __lowerCAmelCase : List[Any]=8 , __lowerCAmelCase : List[str]=["stage1", "stage2"] , __lowerCAmelCase : str=[1, 2] , ) -> List[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = hidden_sizes
a = depths
a = num_heads
a = window_size
a = mlp_ratio
a = qkv_bias
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = drop_path_rate
a = hidden_act
a = use_absolute_embeddings
a = patch_norm
a = layer_norm_eps
a = initializer_range
a = is_training
a = scope
a = use_labels
a = type_sequence_label_size
a = encoder_stride
a = out_features
a = out_indices
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Any ) -> Tuple:
"""simple docstring"""
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def A ( self : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
a = FocalNetModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
a = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
a = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def A ( self : Optional[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : int ) -> int:
"""simple docstring"""
a = FocalNetBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# 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
a = None
a = FocalNetBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self : Union[str, Any] , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
a = FocalNetForMaskedImageModeling(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
a = 1
a = FocalNetForMaskedImageModeling(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
a = self.type_sequence_label_size
a = FocalNetForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
a = 1
a = FocalNetForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A ( self : Tuple ) -> int:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> str:
"""simple docstring"""
a = FocalNetModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , embed_dim=37 , has_text_modality=__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
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 : str ) -> List[Any]:
"""simple docstring"""
return
def A ( self : Dict ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCAmelCase )
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def A ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
a = model_class(__lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowerCAmelCase , nn.Linear ) )
def A ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.hidden_states
a = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__lowerCAmelCase ) , __lowerCAmelCase )
# FocalNet has a different seq_length
a = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
a = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
a = outputs.reshaped_hidden_states
self.assertEqual(len(__lowerCAmelCase ) , __lowerCAmelCase )
a , a , a , a = reshaped_hidden_states[0].shape
a = (
reshaped_hidden_states[0].view(__lowerCAmelCase , __lowerCAmelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
a = True
self.check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
self.check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = 3
a = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
a = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
a = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
a = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
a = True
self.check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
self.check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , (padded_height, padded_width) )
@slow
def A ( self : List[str] ) -> str:
"""simple docstring"""
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = FocalNetModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : Tuple ) -> str:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = _config_zero_init(__lowerCAmelCase )
for model_class in self.all_model_classes:
a = model_class(config=__lowerCAmelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Tuple ) -> int:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def A ( self : str ) -> Dict:
"""simple docstring"""
a = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
a = image_processor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 281 )
@require_torch
class _lowercase ( UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (FocalNetBackbone,) if is_torch_available() else ()
_UpperCAmelCase = FocalNetConfig
_UpperCAmelCase = False
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = FocalNetModelTester(self )
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 1
|
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _lowercase ( unittest.TestCase ):
def __init__( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Tuple=7 , __lowerCAmelCase : Optional[Any]=3 , __lowerCAmelCase : str=18 , __lowerCAmelCase : List[Any]=30 , __lowerCAmelCase : Tuple=400 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : int=True , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Any=True , __lowerCAmelCase : Dict=[0.5, 0.5, 0.5] , __lowerCAmelCase : Optional[int]=[0.5, 0.5, 0.5] , ) -> Any:
"""simple docstring"""
a = size if size is not None else {"shortest_edge": 18}
a = crop_size if crop_size is not None else {"height": 18, "width": 18}
a = parent
a = batch_size
a = num_channels
a = image_size
a = min_resolution
a = max_resolution
a = do_resize
a = size
a = do_center_crop
a = crop_size
a = do_normalize
a = image_mean
a = image_std
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class _lowercase ( UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = LevitImageProcessor if is_vision_available() else None
def A ( self : int ) -> Any:
"""simple docstring"""
a = LevitImageProcessingTester(self )
@property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : int ) -> Any:
"""simple docstring"""
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCAmelCase , "image_mean" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "image_std" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "do_normalize" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "do_resize" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "do_center_crop" ) )
self.assertTrue(hasattr(__lowerCAmelCase , "size" ) )
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def A ( self : Any ) -> int:
"""simple docstring"""
pass
def A ( self : int ) -> List[Any]:
"""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.crop_size["height"],
self.image_processor_tester.crop_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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def A ( self : Tuple ) -> Dict:
"""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.crop_size["height"],
self.image_processor_tester.crop_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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def A ( self : int ) -> Optional[int]:
"""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.crop_size["height"],
self.image_processor_tester.crop_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.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 32
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 1
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 1
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Optional[Any] = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any]=224 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Union[str, Any]=3 , __lowerCAmelCase : Union[str, Any]=96 , __lowerCAmelCase : Optional[int]=[2, 2, 6, 2] , __lowerCAmelCase : Any=[3, 6, 12, 24] , __lowerCAmelCase : Dict=7 , __lowerCAmelCase : Optional[Any]=4.0 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Optional[int]=0.0_2 , __lowerCAmelCase : Dict=1E-5 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : int=None , __lowerCAmelCase : Any=None , **__lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = depths
a = len(__lowerCAmelCase )
a = num_heads
a = window_size
a = mlp_ratio
a = qkv_bias
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = drop_path_rate
a = hidden_act
a = use_absolute_embeddings
a = layer_norm_eps
a = initializer_range
a = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
a = int(embed_dim * 2 ** (len(__lowerCAmelCase ) - 1) )
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(__lowerCAmelCase ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = version.parse('''1.11''' )
@property
def A ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def A ( self : Optional[int] ) -> float:
"""simple docstring"""
return 1E-4
| 32
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 1
|
from collections import defaultdict
from math import gcd
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 1_50_00_00 ):
'''simple docstring'''
a = defaultdict(UpperCAmelCase__ )
a = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , UpperCAmelCase__ , 2 ):
if gcd(UpperCAmelCase__ , UpperCAmelCase__ ) > 1:
continue
a = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(UpperCAmelCase__ , limit + 1 , UpperCAmelCase__ ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
|
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 _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
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=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , 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] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
| 1
|
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''M-CLIP'''
def __init__( self : Tuple , __lowerCAmelCase : Tuple=1024 , __lowerCAmelCase : int=768 , **__lowerCAmelCase : List[Any] ) -> Dict:
"""simple docstring"""
a = transformerDimSize
a = imageDimSize
super().__init__(**__lowerCAmelCase )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = MCLIPConfig
def __init__( self : Optional[Any] , __lowerCAmelCase : int , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
super().__init__(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase )
a = XLMRobertaModel(__lowerCAmelCase )
a = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
a = self.transformer(input_ids=__lowerCAmelCase , attention_mask=__lowerCAmelCase )[0]
a = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(__lowerCAmelCase ), embs
| 32
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 1
|
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (DDPMParallelScheduler,)
def A ( self : Optional[int] , **__lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**__lowerCAmelCase )
return config
def A ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : List[str] ) -> int:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=__lowerCAmelCase , beta_end=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__lowerCAmelCase )
def A ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , )
def A ( self : str ) -> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=__lowerCAmelCase )
def A ( self : Tuple ) -> Any:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = len(__lowerCAmelCase )
a = self.dummy_model()
a = self.dummy_sample_deter
a = self.dummy_sample_deter + 0.1
a = self.dummy_sample_deter - 0.1
a = samplea.shape[0]
a = torch.stack([samplea, samplea, samplea] , dim=0 )
a = torch.arange(__lowerCAmelCase )[0:3, None].repeat(1 , __lowerCAmelCase )
a = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
a = scheduler.batch_step_no_noise(__lowerCAmelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
a = torch.sum(torch.abs(__lowerCAmelCase ) )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3
def A ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = len(__lowerCAmelCase )
a = self.dummy_model()
a = self.dummy_sample_deter
a = torch.manual_seed(0 )
for t in reversed(range(__lowerCAmelCase ) ):
# 1. predict noise residual
a = model(__lowerCAmelCase , __lowerCAmelCase )
# 2. predict previous mean of sample x_t-1
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
a = pred_prev_sample
a = torch.sum(torch.abs(__lowerCAmelCase ) )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3
def A ( self : Any ) -> Any:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(prediction_type="v_prediction" )
a = scheduler_class(**__lowerCAmelCase )
a = len(__lowerCAmelCase )
a = self.dummy_model()
a = self.dummy_sample_deter
a = torch.manual_seed(0 )
for t in reversed(range(__lowerCAmelCase ) ):
# 1. predict noise residual
a = model(__lowerCAmelCase , __lowerCAmelCase )
# 2. predict previous mean of sample x_t-1
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
a = pred_prev_sample
a = torch.sum(torch.abs(__lowerCAmelCase ) )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3
def A ( self : Dict ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__lowerCAmelCase )
a = scheduler.timesteps
for i, timestep in enumerate(__lowerCAmelCase ):
if i == len(__lowerCAmelCase ) - 1:
a = -1
else:
a = timesteps[i + 1]
a = scheduler.previous_timestep(__lowerCAmelCase )
a = prev_t.item()
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : Tuple ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = [100, 87, 50, 51, 0]
with self.assertRaises(__lowerCAmelCase , msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=__lowerCAmelCase )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = [100, 87, 50, 1, 0]
a = len(__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase , timesteps=__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__lowerCAmelCase , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=__lowerCAmelCase )
| 32
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 1
|
import socket
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
a = socket.gethostname()
a = 1_23_12
sock.connect((host, port) )
sock.send(b"Hello server!" )
with open("Received_file" , "wb" ) as out_file:
print("File opened" )
print("Receiving data..." )
while True:
a = sock.recv(10_24 )
if not data:
break
out_file.write(UpperCAmelCase__ )
print("Successfully received the file" )
sock.close()
print("Connection closed" )
if __name__ == "__main__":
main()
| 32
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : Optional[int] = logging.get_logger(__name__)
A_ : List[Any] = '''▁'''
A_ : Tuple = {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}
A_ : int = {
'''vocab_file''': {
'''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
},
'''monolingual_vocab_file''': {
'''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
},
}
A_ : Union[str, Any] = {'''vinai/bartpho-syllable''': 10_24}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : Dict="<s>" , __lowerCAmelCase : Tuple="</s>" , __lowerCAmelCase : Optional[int]="</s>" , __lowerCAmelCase : int="<s>" , __lowerCAmelCase : Optional[int]="<unk>" , __lowerCAmelCase : List[Any]="<pad>" , __lowerCAmelCase : Dict="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : int , ) -> None:
"""simple docstring"""
a = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
a = vocab_file
a = monolingual_vocab_file
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
a = {}
a = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
a = cnt
cnt += 1
with open(__lowerCAmelCase , "r" , encoding="utf-8" ) as f:
for line in f.readlines():
a = line.strip().split()[0]
a = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
a = len(self.fairseq_tokens_to_ids )
a = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Dict ) -> Tuple:
"""simple docstring"""
a = self.__dict__.copy()
a = None
a = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : List[str] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def A ( self : int , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
a = [self.cls_token_id]
a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def A ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def A ( self : Tuple ) -> Any:
"""simple docstring"""
return len(self.fairseq_ids_to_tokens )
def A ( self : Tuple ) -> Tuple:
"""simple docstring"""
a = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def A ( self : List[Any] , __lowerCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.fairseq_ids_to_tokens[index]
def A ( self : int , __lowerCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
a = "".join(__lowerCAmelCase ).replace(__lowerCAmelCase , " " ).strip()
return out_string
def A ( self : Any , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
a = os.path.join(
__lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , "wb" ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , "w" , encoding="utf-8" ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f"""{str(__lowerCAmelCase )} \n""" )
return out_vocab_file, out_monolingual_vocab_file
| 32
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''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()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 1
|
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = 1
@register_to_config
def __init__( self : List[Any] , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : float = 0.1_5 , __lowerCAmelCase : float = 0.0_1 , __lowerCAmelCase : float = 1_3_4_8.0 , __lowerCAmelCase : float = 1E-5 , __lowerCAmelCase : int = 1 , ) -> Optional[Any]:
"""simple docstring"""
a = sigma_max
# setable values
a = None
self.set_sigmas(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : int , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[int] = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def A ( self : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : float = None , __lowerCAmelCase : Union[str, torch.device] = None ) -> Optional[Any]:
"""simple docstring"""
a = sampling_eps if sampling_eps is not None else self.config.sampling_eps
a = torch.linspace(1 , __lowerCAmelCase , __lowerCAmelCase , device=__lowerCAmelCase )
def A ( self : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : float = None , __lowerCAmelCase : float = None , __lowerCAmelCase : float = None ) -> Union[str, Any]:
"""simple docstring"""
a = sigma_min if sigma_min is not None else self.config.sigma_min
a = sigma_max if sigma_max is not None else self.config.sigma_max
a = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(__lowerCAmelCase , __lowerCAmelCase )
a = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
a = torch.exp(torch.linspace(math.log(__lowerCAmelCase ) , math.log(__lowerCAmelCase ) , __lowerCAmelCase ) )
a = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def A ( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def A ( self : List[str] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : int , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : bool = True , ) -> Union[SdeVeOutput, Tuple]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
a = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
a = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
a = timesteps.to(self.discrete_sigmas.device )
a = self.discrete_sigmas[timesteps].to(sample.device )
a = self.get_adjacent_sigma(__lowerCAmelCase , __lowerCAmelCase ).to(sample.device )
a = torch.zeros_like(__lowerCAmelCase )
a = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
a = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
a = diffusion.unsqueeze(-1 )
a = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
a = randn_tensor(
sample.shape , layout=sample.layout , generator=__lowerCAmelCase , device=sample.device , dtype=sample.dtype )
a = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
a = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=__lowerCAmelCase , prev_sample_mean=__lowerCAmelCase )
def A ( self : Optional[Any] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
"`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
a = randn_tensor(sample.shape , layout=sample.layout , generator=__lowerCAmelCase ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
a = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
a = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
a = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
a = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
a = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
a = step_size.unsqueeze(-1 )
a = sample + step_size * model_output
a = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , __lowerCAmelCase : torch.FloatTensor , ) -> torch.FloatTensor:
"""simple docstring"""
a = timesteps.to(original_samples.device )
a = self.discrete_sigmas.to(original_samples.device )[timesteps]
a = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(__lowerCAmelCase ) * sigmas[:, None, None, None]
)
a = noise + original_samples
return noisy_samples
def __len__( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
return self.config.num_train_timesteps
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 1
|
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A_ : Union[str, Any] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''AutoTokenizer'''
_UpperCAmelCase = ['''tokenizer''']
_UpperCAmelCase = {
'''semantic_prompt''': 1,
'''coarse_prompt''': 2,
'''fine_prompt''': 2,
}
def __init__( self : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any]=None ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(__lowerCAmelCase )
a = speaker_embeddings
@classmethod
def A ( cls : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any]="speaker_embeddings_path.json" , **__lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
if speaker_embeddings_dict_path is not None:
a = get_file_from_repo(
__lowerCAmelCase , __lowerCAmelCase , subfolder=kwargs.pop("subfolder" , __lowerCAmelCase ) , cache_dir=kwargs.pop("cache_dir" , __lowerCAmelCase ) , force_download=kwargs.pop("force_download" , __lowerCAmelCase ) , proxies=kwargs.pop("proxies" , __lowerCAmelCase ) , resume_download=kwargs.pop("resume_download" , __lowerCAmelCase ) , local_files_only=kwargs.pop("local_files_only" , __lowerCAmelCase ) , use_auth_token=kwargs.pop("use_auth_token" , __lowerCAmelCase ) , revision=kwargs.pop("revision" , __lowerCAmelCase ) , )
if speaker_embeddings_path is None:
logger.warning(
f"""`{os.path.join(__lowerCAmelCase , __lowerCAmelCase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.""" )
a = None
else:
with open(__lowerCAmelCase ) as speaker_embeddings_json:
a = json.load(__lowerCAmelCase )
else:
a = None
a = AutoTokenizer.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
return cls(tokenizer=__lowerCAmelCase , speaker_embeddings=__lowerCAmelCase )
def A ( self : Tuple , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="speaker_embeddings_path.json" , __lowerCAmelCase : str="speaker_embeddings" , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Dict , ) -> Optional[Any]:
"""simple docstring"""
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(__lowerCAmelCase , __lowerCAmelCase , "v2" ) , exist_ok=__lowerCAmelCase )
a = {}
a = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
a = self._load_voice_preset(__lowerCAmelCase )
a = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , __lowerCAmelCase , f"""{prompt_key}_{key}""" ) , voice_preset[key] , allow_pickle=__lowerCAmelCase , )
a = os.path.join(__lowerCAmelCase , f"""{prompt_key}_{key}.npy""" )
a = tmp_dict
with open(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , "w" ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
super().save_pretrained(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def A ( self : List[Any] , __lowerCAmelCase : str = None , **__lowerCAmelCase : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
a = self.speaker_embeddings[voice_preset]
a = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].""" )
a = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , __lowerCAmelCase ) , cache_dir=kwargs.pop("cache_dir" , __lowerCAmelCase ) , force_download=kwargs.pop("force_download" , __lowerCAmelCase ) , proxies=kwargs.pop("proxies" , __lowerCAmelCase ) , resume_download=kwargs.pop("resume_download" , __lowerCAmelCase ) , local_files_only=kwargs.pop("local_files_only" , __lowerCAmelCase ) , use_auth_token=kwargs.pop("use_auth_token" , __lowerCAmelCase ) , revision=kwargs.pop("revision" , __lowerCAmelCase ) , )
if path is None:
raise ValueError(
f"""`{os.path.join(self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.""" )
a = np.load(__lowerCAmelCase )
return voice_preset_dict
def A ( self : Optional[int] , __lowerCAmelCase : Optional[dict] = None ) -> Dict:
"""simple docstring"""
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f"""Voice preset unrecognized, missing {key} as a key.""" )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f"""{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.""" )
def __call__( self : List[Any] , __lowerCAmelCase : str=None , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Union[str, Any]="pt" , __lowerCAmelCase : List[Any]=256 , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : Any=True , __lowerCAmelCase : List[str]=False , **__lowerCAmelCase : List[Any] , ) -> Tuple:
"""simple docstring"""
if voice_preset is not None and not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if (
isinstance(__lowerCAmelCase , __lowerCAmelCase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
a = self._load_voice_preset(__lowerCAmelCase )
else:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ) and not voice_preset.endswith(".npz" ):
a = voice_preset + ".npz"
a = np.load(__lowerCAmelCase )
if voice_preset is not None:
self._validate_voice_preset_dict(__lowerCAmelCase , **__lowerCAmelCase )
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
a = self.tokenizer(
__lowerCAmelCase , return_tensors=__lowerCAmelCase , padding="max_length" , max_length=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase , **__lowerCAmelCase , )
if voice_preset is not None:
a = voice_preset
return encoded_text
| 32
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''falcon'''
_UpperCAmelCase = ['''past_key_values''']
def __init__( self : int , __lowerCAmelCase : Tuple=6_5024 , __lowerCAmelCase : int=4544 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Union[str, Any]=71 , __lowerCAmelCase : Any=1E-5 , __lowerCAmelCase : Union[str, Any]=0.0_2 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : List[str]=False , __lowerCAmelCase : Dict=11 , __lowerCAmelCase : Any=11 , **__lowerCAmelCase : Any , ) -> int:
"""simple docstring"""
a = vocab_size
# Backward compatibility with n_embed kwarg
a = kwargs.pop("n_embed" , __lowerCAmelCase )
a = hidden_size if n_embed is None else n_embed
a = num_hidden_layers
a = num_attention_heads
a = layer_norm_epsilon
a = initializer_range
a = use_cache
a = hidden_dropout
a = attention_dropout
a = bos_token_id
a = eos_token_id
a = num_attention_heads if num_kv_heads is None else num_kv_heads
a = alibi
a = new_decoder_architecture
a = multi_query # Ignored when new_decoder_architecture is True
a = parallel_attn
a = bias
super().__init__(bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
@property
def A ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return self.hidden_size // self.num_attention_heads
@property
def A ( self : Dict ) -> Tuple:
"""simple docstring"""
return not self.alibi
| 32
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 1
|
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A_ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
A_ : str = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
A_ : List[str] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :str ):
'''simple docstring'''
a = len([g for position, g in enumerate(UpperCAmelCase__ ) if g == main_target[position]] )
return (item, float(UpperCAmelCase__ ))
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :str ):
'''simple docstring'''
a = random.randint(0 , len(UpperCAmelCase__ ) - 1 )
a = parent_a[:random_slice] + parent_a[random_slice:]
a = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :list[str] ):
'''simple docstring'''
a = list(UpperCAmelCase__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
a = random.choice(UpperCAmelCase__ )
return "".join(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :tuple[str, float] , UpperCAmelCase__ :list[tuple[str, float]] , UpperCAmelCase__ :list[str] , ):
'''simple docstring'''
a = []
# Generate more children proportionally to the fitness score.
a = int(parent_a[1] * 1_00 ) + 1
a = 10 if child_n >= 10 else child_n
for _ in range(UpperCAmelCase__ ):
a = population_score[random.randint(0 , UpperCAmelCase__ )][0]
a , a = crossover(parent_a[0] , UpperCAmelCase__ )
# Append new string to the population list.
pop.append(mutate(UpperCAmelCase__ , UpperCAmelCase__ ) )
pop.append(mutate(UpperCAmelCase__ , UpperCAmelCase__ ) )
return pop
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :list[str] , UpperCAmelCase__ :bool = True ):
'''simple docstring'''
if N_POPULATION < N_SELECTED:
a = F"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(UpperCAmelCase__ )
# Verify that the target contains no genes besides the ones inside genes variable.
a = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
a = F"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(UpperCAmelCase__ )
# Generate random starting population.
a = []
for _ in range(UpperCAmelCase__ ):
population.append("".join([random.choice(UpperCAmelCase__ ) for i in range(len(UpperCAmelCase__ ) )] ) )
# Just some logs to know what the algorithms is doing.
a , a = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(UpperCAmelCase__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
a = [evaluate(UpperCAmelCase__ , UpperCAmelCase__ ) for item in population]
# Check if there is a matching evolution.
a = sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__ : x[1] , reverse=UpperCAmelCase__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F"""\nGeneration: {generation}"""
F"""\nTotal Population:{total_population}"""
F"""\nBest score: {population_score[0][1]}"""
F"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
a = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(UpperCAmelCase__ )
# Normalize population score to be between 0 and 1.
a = [
(item, score / len(UpperCAmelCase__ )) for item, score in population_score
]
# This is selection
for i in range(UpperCAmelCase__ ):
population.extend(select(population_score[int(UpperCAmelCase__ )] , UpperCAmelCase__ , UpperCAmelCase__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(UpperCAmelCase__ ) > N_POPULATION:
break
if __name__ == "__main__":
A_ : int = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
A_ : Dict = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
A_ , A_ , A_ : int = basic(target_str, genes_list)
print(
F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
)
| 32
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
a = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = StableDiffusionLatentUpscalePipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''height''',
'''width''',
'''cross_attention_kwargs''',
'''negative_prompt_embeds''',
'''prompt_embeds''',
}
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_UpperCAmelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_UpperCAmelCase = frozenset([] )
_UpperCAmelCase = True
@property
def A ( self : Any ) -> str:
"""simple docstring"""
a = 1
a = 4
a = (16, 16)
a = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(__lowerCAmelCase )
return image
def A ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
a = UNetaDConditionModel(
act_fn="gelu" , attention_head_dim=8 , norm_num_groups=__lowerCAmelCase , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"KDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
"KCrossAttnDownBlock2D",
) , in_channels=8 , mid_block_type=__lowerCAmelCase , only_cross_attention=__lowerCAmelCase , out_channels=5 , resnet_time_scale_shift="scale_shift" , time_embedding_type="fourier" , timestep_post_act="gelu" , up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D") , )
a = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
a = EulerDiscreteScheduler(prediction_type="sample" )
a = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="quick_gelu" , projection_dim=512 , )
a = CLIPTextModel(__lowerCAmelCase )
a = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
a = {
"unet": model.eval(),
"vae": vae.eval(),
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def A ( self : Union[str, Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any]=0 ) -> Optional[int]:
"""simple docstring"""
if str(__lowerCAmelCase ).startswith("mps" ):
a = torch.manual_seed(__lowerCAmelCase )
else:
a = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
a = {
"prompt": "A painting of a squirrel eating a burger",
"image": self.dummy_image.cpu(),
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = "cpu"
a = self.get_dummy_components()
a = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
a = np.array(
[0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5] )
a = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCAmelCase , 1E-3 )
def A ( self : Any ) -> int:
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )
def A ( self : str ) -> Optional[Any]:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=7E-3 )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=3E-3 )
def A ( self : int ) -> str:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def A ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
a = [
"DDIMScheduler",
"DDPMScheduler",
"PNDMScheduler",
"HeunDiscreteScheduler",
"EulerAncestralDiscreteScheduler",
"KDPM2DiscreteScheduler",
"KDPM2AncestralDiscreteScheduler",
"DPMSolverSDEScheduler",
]
a = self.get_dummy_components()
a = self.pipeline_class(**__lowerCAmelCase )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = 2
a = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
a = getattr(__lowerCAmelCase , scheduler_enum.name )
a = scheduler_cls.from_config(pipe.scheduler.config )
a = pipe(**__lowerCAmelCase )[0]
outputs.append(__lowerCAmelCase )
assert check_same_shape(__lowerCAmelCase )
@require_torch_gpu
@slow
class _lowercase ( unittest.TestCase ):
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Dict ) -> Any:
"""simple docstring"""
a = torch.manual_seed(33 )
a = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" , torch_dtype=torch.floataa )
pipe.to("cuda" )
a = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler" , torch_dtype=torch.floataa )
upscaler.to("cuda" )
a = "a photo of an astronaut high resolution, unreal engine, ultra realistic"
a = pipe(__lowerCAmelCase , generator=__lowerCAmelCase , output_type="latent" ).images
a = upscaler(
prompt=__lowerCAmelCase , image=__lowerCAmelCase , num_inference_steps=20 , guidance_scale=0 , generator=__lowerCAmelCase , output_type="np" , ).images[0]
a = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy" )
assert np.abs((expected_image - image).mean() ) < 5E-2
def A ( self : int ) -> Any:
"""simple docstring"""
a = torch.manual_seed(33 )
a = StableDiffusionLatentUpscalePipeline.from_pretrained(
"stabilityai/sd-x2-latent-upscaler" , torch_dtype=torch.floataa )
upscaler.to("cuda" )
a = "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"
a = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png" )
a = upscaler(
prompt=__lowerCAmelCase , image=__lowerCAmelCase , num_inference_steps=20 , guidance_scale=0 , generator=__lowerCAmelCase , output_type="np" , ).images[0]
a = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy" )
assert np.abs((expected_image - image).max() ) < 5E-2
| 32
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 1
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 1
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : Dict = {
'''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''levit'''
def __init__( self : str , __lowerCAmelCase : List[str]=224 , __lowerCAmelCase : int=3 , __lowerCAmelCase : List[Any]=3 , __lowerCAmelCase : List[str]=2 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : Union[str, Any]=16 , __lowerCAmelCase : Tuple=[128, 256, 384] , __lowerCAmelCase : Union[str, Any]=[4, 8, 12] , __lowerCAmelCase : List[str]=[4, 4, 4] , __lowerCAmelCase : Any=[16, 16, 16] , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : Any=[2, 2, 2] , __lowerCAmelCase : Tuple=[2, 2, 2] , __lowerCAmelCase : Dict=0.0_2 , **__lowerCAmelCase : int , ) -> Dict:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = num_channels
a = kernel_size
a = stride
a = padding
a = hidden_sizes
a = num_attention_heads
a = depths
a = key_dim
a = drop_path_rate
a = patch_size
a = attention_ratio
a = mlp_ratio
a = initializer_range
a = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = version.parse('''1.11''' )
@property
def A ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def A ( self : str ) -> float:
"""simple docstring"""
return 1E-4
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 1
|
from math import isqrt, loga
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCAmelCase__ , UpperCAmelCase__ ):
a = False
return [i for i in range(2 , UpperCAmelCase__ ) if is_prime[i]]
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 80_08_00 , UpperCAmelCase__ :int = 80_08_00 ):
'''simple docstring'''
a = degree * loga(UpperCAmelCase__ )
a = int(UpperCAmelCase__ )
a = calculate_prime_numbers(UpperCAmelCase__ )
a = 0
a = 0
a = len(UpperCAmelCase__ ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 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 _lowercase ( unittest.TestCase ):
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
a = tempfile.mkdtemp()
# fmt: off
a = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
# fmt: on
a = 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] ) )
a = {
"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],
}
a = os.path.join(self.tmpdirname , __lowerCAmelCase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : Dict , **__lowerCAmelCase : Any ) -> Tuple:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def A ( self : List[Any] , **__lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
a = [Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = self.get_tokenizer()
a = self.get_image_processor()
a = VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
a = 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 , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
a = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
a = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 )
a = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowerCAmelCase , 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 , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
a = self.prepare_image_inputs()
a = image_processor(__lowerCAmelCase , return_tensors="np" )
a = processor(images=__lowerCAmelCase , 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] ) -> Optional[int]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
a = "lower newer"
a = processor(text=__lowerCAmelCase )
a = tokenizer(__lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
a = "lower newer"
a = self.prepare_image_inputs()
a = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
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(__lowerCAmelCase ):
processor()
def A ( self : Optional[Any] ) -> str:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
a = processor.batch_decode(__lowerCAmelCase )
a = tokenizer.batch_decode(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a = self.get_image_processor()
a = self.get_tokenizer()
a = VisionTextDualEncoderProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase )
a = "lower newer"
a = self.prepare_image_inputs()
a = processor(text=__lowerCAmelCase , images=__lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 1
|
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, 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 _lowercase :
@staticmethod
def A ( *__lowerCAmelCase : str , **__lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_torch
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
def A ( self : Tuple , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
a = [
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
]
return object_detector, examples
def A ( self : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = object_detector(examples[0] , threshold=0.0 )
a = len(__lowerCAmelCase )
self.assertGreater(__lowerCAmelCase , 0 )
self.assertEqual(
__lowerCAmelCase , [
{
"score": ANY(__lowerCAmelCase ),
"label": ANY(__lowerCAmelCase ),
"box": {"xmin": ANY(__lowerCAmelCase ), "ymin": ANY(__lowerCAmelCase ), "xmax": ANY(__lowerCAmelCase ), "ymax": ANY(__lowerCAmelCase )},
}
for i in range(__lowerCAmelCase )
] , )
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF" )
def A ( self : Any ) -> List[Any]:
"""simple docstring"""
pass
@require_torch
def A ( self : int ) -> Dict:
"""simple docstring"""
a = pipeline(
"zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" )
a = object_detector(
"./tests/fixtures/tests_samples/COCO/000000039769.png" , candidate_labels=["cat", "remote", "couch"] , threshold=0.6_4 , )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [
{"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
] , )
a = object_detector(
[
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"candidate_labels": ["cat", "remote", "couch"],
}
] , threshold=0.6_4 , )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [
[
{"score": 0.7_2_3_5, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_2_1_8, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.7_1_8_4, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
{"score": 0.6_7_4_8, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_5_6, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_6_1_4, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
{"score": 0.6_4_5_6, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
{"score": 0.6_4_2, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
{"score": 0.6_4_1_9, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
]
] , )
@require_torch
@slow
def A ( self : str ) -> int:
"""simple docstring"""
a = pipeline("zero-shot-object-detection" )
a = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
] , )
a = object_detector(
[
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
{
"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
"candidate_labels": ["cat", "remote", "couch"],
},
] , )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [
[
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
],
[
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
{"score": 0.1_4_7_4, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
{"score": 0.1_2_0_8, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
],
] , )
@require_tf
@unittest.skip("Zero Shot Object Detection not implemented in TF" )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
pass
@require_torch
@slow
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
a = 0.2
a = pipeline("zero-shot-object-detection" )
a = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , threshold=__lowerCAmelCase , )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
{"score": 0.2_5_3_7, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
] , )
@require_torch
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a = 2
a = pipeline("zero-shot-object-detection" )
a = object_detector(
"http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , top_k=__lowerCAmelCase , )
self.assertEqual(
nested_simplify(__lowerCAmelCase , decimals=4 ) , [
{"score": 0.2_8_6_8, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
{"score": 0.2_7_7, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
] , )
| 32
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 1
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[Any] = {
'''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_ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Tuple = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
A_ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 1
|
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
A_ : List[Any] = logging.get_logger(__name__)
A_ : List[Any] = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
A_ : List[str] = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
A_ : List[str] = {
'''facebook/blenderbot_small-90M''': 5_12,
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = BlenderbotSmallTokenizer
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Tuple=None , __lowerCAmelCase : Any="<|endoftext|>" , __lowerCAmelCase : Tuple="<|endoftext|>" , __lowerCAmelCase : str="<|endoftext|>" , __lowerCAmelCase : Any=False , __lowerCAmelCase : Union[str, Any]=True , **__lowerCAmelCase : int , ) -> str:
"""simple docstring"""
super().__init__(
ByteLevelBPETokenizer(
vocab=__lowerCAmelCase , merges=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase , trim_offsets=__lowerCAmelCase , ) , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , **__lowerCAmelCase , )
a = add_prefix_space
def A ( self : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str]=None ) -> List[Any]:
"""simple docstring"""
a = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A ( self : int , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 32
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[Any] = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''DistilBertConfig''',
'''DistilBertOnnxConfig''',
],
'''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = ['''DistilBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
'''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DistilBertForMaskedLM''',
'''DistilBertForMultipleChoice''',
'''DistilBertForQuestionAnswering''',
'''DistilBertForSequenceClassification''',
'''DistilBertForTokenClassification''',
'''DistilBertModel''',
'''DistilBertPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFDistilBertForMaskedLM''',
'''TFDistilBertForMultipleChoice''',
'''TFDistilBertForQuestionAnswering''',
'''TFDistilBertForSequenceClassification''',
'''TFDistilBertForTokenClassification''',
'''TFDistilBertMainLayer''',
'''TFDistilBertModel''',
'''TFDistilBertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''FlaxDistilBertForMaskedLM''',
'''FlaxDistilBertForMultipleChoice''',
'''FlaxDistilBertForQuestionAnswering''',
'''FlaxDistilBertForSequenceClassification''',
'''FlaxDistilBertForTokenClassification''',
'''FlaxDistilBertModel''',
'''FlaxDistilBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
A_ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 1
|
import os
import time
import numpy as np
import onnxruntime as ort
A_ : List[Any] = '''1'''
A_ : Tuple = '''0'''
A_ : Optional[int] = '''1'''
A_ : Union[str, Any] = ort.SessionOptions()
A_ : List[str] = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
A_ : Optional[Any] = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
A_ : Union[str, Any] = ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
A_ : Optional[Any] = ort.RunOptions()
A_ : List[str] = 1_28
A_ : Tuple = 1
A_ : str = np.ones((batch, sequence), dtype=np.intaa)
A_ : Optional[Any] = np.ones((batch, sequence), dtype=np.intaa)
A_ : int = np.ones((batch, sequence), dtype=np.intaa)
print('''Warm up phase...''')
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('''Start inference...''')
A_ : int = time.time()
A_ : Dict = 20_00
A_ : Union[str, Any] = {}
for iter in range(max_iters):
A_ : Optional[int] = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 10_00 / max_iters))
| 32
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 1
|
from pathlib import Path
import numpy as np
from PIL import Image
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
a , a , a = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
return (gray > 1_27) & (gray <= 2_55)
def UpperCAmelCase__ ( UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ):
'''simple docstring'''
a = np.zeros_like(UpperCAmelCase__ )
a = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
a = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
a = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
a = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
A_ : int = Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
A_ : int = np.array(Image.open(lena_path))
# kernel to be applied
A_ : int = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
A_ : Any = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
A_ : str = Image.fromarray(output).convert('''RGB''')
pil_img.save('''result_dilation.png''')
| 32
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 1
|
from math import isclose, sqrt
def UpperCAmelCase__ ( UpperCAmelCase__ :float , UpperCAmelCase__ :float , UpperCAmelCase__ :float ):
'''simple docstring'''
a = point_y / 4 / point_x
a = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
a = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
a = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
a = outgoing_gradient**2 + 4
a = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
a = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
a = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
a = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
a = x_minus if isclose(UpperCAmelCase__ , UpperCAmelCase__ ) else x_plus
a = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def UpperCAmelCase__ ( UpperCAmelCase__ :float = 1.4 , UpperCAmelCase__ :float = -9.6 ):
'''simple docstring'''
a = 0
a = first_x_coord
a = first_y_coord
a = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
a , a , a = next_point(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
|
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 _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
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=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , 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] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
| 1
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Any = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''pix2struct_text_model'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Optional[int] , __lowerCAmelCase : int=5_0244 , __lowerCAmelCase : str=768 , __lowerCAmelCase : List[Any]=64 , __lowerCAmelCase : Union[str, Any]=2048 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : str=128 , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : int=1E-6 , __lowerCAmelCase : str=1.0 , __lowerCAmelCase : Optional[int]="gelu_new" , __lowerCAmelCase : List[str]=0 , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : Optional[int]=0 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : int=True , **__lowerCAmelCase : List[str] , ) -> Any:
"""simple docstring"""
a = vocab_size
a = hidden_size
a = d_kv
a = d_ff
a = num_layers
a = num_heads
a = relative_attention_num_buckets
a = relative_attention_max_distance
a = dropout_rate
a = layer_norm_epsilon
a = initializer_factor
a = use_cache
a = eos_token_id
a = decoder_start_token_id
# for backwards compatibility
a = dense_act_fn
super().__init__(
pad_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , is_decoder=__lowerCAmelCase , **__lowerCAmelCase , )
@classmethod
def A ( cls : Dict , __lowerCAmelCase : Union[str, os.PathLike] , **__lowerCAmelCase : Dict ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__lowerCAmelCase )
a , a = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
a = 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(__lowerCAmelCase , **__lowerCAmelCase )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''pix2struct_vision_model'''
def __init__( self : List[Any] , __lowerCAmelCase : List[Any]=768 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Optional[int]=2048 , __lowerCAmelCase : int=64 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : str=12 , __lowerCAmelCase : Optional[Any]="gelu_new" , __lowerCAmelCase : Any=1E-6 , __lowerCAmelCase : Optional[Any]=0.0 , __lowerCAmelCase : Tuple=0.0 , __lowerCAmelCase : int=1E-10 , __lowerCAmelCase : int=1.0 , __lowerCAmelCase : Optional[Any]=4096 , __lowerCAmelCase : str=32 , __lowerCAmelCase : Any=128 , **__lowerCAmelCase : Optional[Any] , ) -> Tuple:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = hidden_size
a = patch_embed_hidden_size
a = d_ff
a = dropout_rate
a = num_hidden_layers
a = num_attention_heads
a = initializer_range
a = initializer_factor
a = attention_dropout
a = layer_norm_eps
a = dense_act_fn
a = seq_len
a = relative_attention_num_buckets
a = relative_attention_max_distance
a = d_kv
@classmethod
def A ( cls : List[str] , __lowerCAmelCase : Union[str, os.PathLike] , **__lowerCAmelCase : str ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__lowerCAmelCase )
a , a = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("model_type" ) == "pix2struct":
a = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''pix2struct'''
_UpperCAmelCase = True
def __init__( self : Optional[Any] , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : Optional[Any]=1.0 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : Any , ) -> List[Any]:
"""simple docstring"""
super().__init__(tie_word_embeddings=__lowerCAmelCase , is_encoder_decoder=__lowerCAmelCase , **__lowerCAmelCase )
if text_config is None:
a = {}
logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )
if vision_config is None:
a = {}
logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )
a = PixaStructTextConfig(**__lowerCAmelCase )
a = PixaStructVisionConfig(**__lowerCAmelCase )
a = self.text_config.decoder_start_token_id
a = self.text_config.pad_token_id
a = self.text_config.eos_token_id
a = initializer_factor
a = initializer_range
a = self.initializer_range
a = self.initializer_range
a = is_vqa
@classmethod
def A ( cls : Optional[Any] , __lowerCAmelCase : PixaStructTextConfig , __lowerCAmelCase : PixaStructVisionConfig , **__lowerCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__lowerCAmelCase )
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
a = copy.deepcopy(self.__dict__ )
a = self.text_config.to_dict()
a = self.vision_config.to_dict()
a = self.__class__.model_type
return output
| 32
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 1
|
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''flax''', '''transformers''']
def __init__( self : int , *__lowerCAmelCase : Dict , **__lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def A ( cls : Optional[Any] , *__lowerCAmelCase : int , **__lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def A ( cls : List[str] , *__lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''flax''', '''transformers''']
def __init__( self : str , *__lowerCAmelCase : int , **__lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def A ( cls : str , *__lowerCAmelCase : int , **__lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def A ( cls : Optional[Any] , *__lowerCAmelCase : Dict , **__lowerCAmelCase : Tuple ) -> int:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''flax''', '''transformers''']
def __init__( self : List[Any] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def A ( cls : Tuple , *__lowerCAmelCase : int , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def A ( cls : List[Any] , *__lowerCAmelCase : Optional[Any] , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''flax''', '''transformers''']
def __init__( self : List[str] , *__lowerCAmelCase : Tuple , **__lowerCAmelCase : str ) -> List[Any]:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def A ( cls : List[str] , *__lowerCAmelCase : Dict , **__lowerCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def A ( cls : str , *__lowerCAmelCase : Dict , **__lowerCAmelCase : Any ) -> str:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
| 32
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 1
|
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
A_ : Dict = False
A_ : List[str] = True
A_ : Dict = False
if __name__ == "__main__":
A_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--repo_path''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the architecture.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
A_ : List[Any] = parser.parse_args()
A_ : Any = {
'''image_size''': '''sample_size''',
'''num_res_blocks''': '''layers_per_block''',
'''block_channels''': '''block_out_channels''',
'''down_blocks''': '''down_block_types''',
'''up_blocks''': '''up_block_types''',
'''downscale_freq_shift''': '''freq_shift''',
'''resnet_num_groups''': '''norm_num_groups''',
'''resnet_act_fn''': '''act_fn''',
'''resnet_eps''': '''norm_eps''',
'''num_head_channels''': '''attention_head_dim''',
}
A_ : Any = {
'''time_steps''': '''time_proj''',
'''mid''': '''mid_block''',
'''downsample_blocks''': '''down_blocks''',
'''upsample_blocks''': '''up_blocks''',
}
A_ : Optional[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:
A_ : Union[str, Any] = reader.read()
A_ : Optional[int] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, '''config.json'''):
A_ : Optional[int] = UNetaDModel(**config)
else:
A_ : List[Any] = UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
A_ : str = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
A_ : Dict = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
A_ : Optional[int] = config[key]
del config[key]
A_ : Dict = [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
A_ : Optional[int] = [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]
if do_only_weights:
A_ : Tuple = torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))
A_ : Tuple = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
continue
A_ : str = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('''.''')[0] == key:
A_ : Tuple = param_value
A_ : str = True
if not has_changed:
A_ : str = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 32
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
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 _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
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=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , 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] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''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()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
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|
import random
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :List[str] ):
'''simple docstring'''
a , a , a = [], [], []
for element in data:
if element < pivot:
less.append(UpperCAmelCase__ )
elif element > pivot:
greater.append(UpperCAmelCase__ )
else:
equal.append(UpperCAmelCase__ )
return less, equal, greater
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :int ):
'''simple docstring'''
if index >= len(UpperCAmelCase__ ) or index < 0:
return None
a = items[random.randint(0 , len(UpperCAmelCase__ ) - 1 )]
a = 0
a , a , a = _partition(UpperCAmelCase__ , UpperCAmelCase__ )
a = len(UpperCAmelCase__ )
a = len(UpperCAmelCase__ )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(UpperCAmelCase__ , UpperCAmelCase__ )
# must be in larger
else:
return quick_select(UpperCAmelCase__ , index - (m + count) )
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :list , UpperCAmelCase__ :list , UpperCAmelCase__ :int ):
'''simple docstring'''
a = len(UpperCAmelCase__ )
a = [[0] * n for i in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
a = y_points[i]
for i in range(2 , UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ , UpperCAmelCase__ ):
a = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ : int = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[str] = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
A_ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :list[int] , UpperCAmelCase__ :list[int] , UpperCAmelCase__ :int ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :list[list[int]] , UpperCAmelCase__ :int , UpperCAmelCase__ :list[int] , UpperCAmelCase__ :int ):
'''simple docstring'''
if index == len(UpperCAmelCase__ ):
return True
# Recursive Step
for i in range(UpperCAmelCase__ ):
if valid_coloring(graph[index] , UpperCAmelCase__ , UpperCAmelCase__ ):
# Color current vertex
a = i
# Validate coloring
if util_color(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , index + 1 ):
return True
# Backtrack
a = -1
return False
def UpperCAmelCase__ ( UpperCAmelCase__ :list[list[int]] , UpperCAmelCase__ :int ):
'''simple docstring'''
a = [-1] * len(UpperCAmelCase__ )
if util_color(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , 0 ):
return colored_vertices
return []
| 32
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 1
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 1
|
from sklearn.metrics import mean_squared_error
import datasets
A_ : int = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
A_ : Optional[int] = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
A_ : str = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def A ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
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 A ( self : Dict ) -> Tuple:
"""simple docstring"""
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 A ( self : Dict , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : str="uniform_average" , __lowerCAmelCase : List[Any]=True ) -> Any:
"""simple docstring"""
a = mean_squared_error(
__lowerCAmelCase , __lowerCAmelCase , sample_weight=__lowerCAmelCase , multioutput=__lowerCAmelCase , squared=__lowerCAmelCase )
return {"mse": mse}
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : List[str] = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
'''TableTransformerOnnxConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Any = [
'''TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TableTransformerForObjectDetection''',
'''TableTransformerModel''',
'''TableTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TableTransformerConfig,
TableTransformerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TableTransformerForObjectDetection,
TableTransformerModel,
TableTransformerPreTrainedModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 1
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
a = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
a = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
a = model(__lowerCAmelCase )["last_hidden_state"]
a = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , __lowerCAmelCase )
# compare the actual values for a slice.
a = tf.convert_to_tensor(
[[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=UpperCAmelCase__ )
a = parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=UpperCAmelCase__ )
env_command_parser(subparsers=UpperCAmelCase__ )
launch_command_parser(subparsers=UpperCAmelCase__ )
tpu_command_parser(subparsers=UpperCAmelCase__ )
test_command_parser(subparsers=UpperCAmelCase__ )
# Let's go
a = parser.parse_args()
if not hasattr(UpperCAmelCase__ , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 32
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 1
|
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowercase ( unittest.TestCase, UpperCAmelCase__ ):
def A ( self : List[Any] ) -> Tuple:
"""simple docstring"""
a = load_tool("text-classification" )
self.tool.setup()
a = load_tool("text-classification" , remote=__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
def A ( self : Any ) -> Dict:
"""simple docstring"""
a = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
a = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
def A ( self : Any ) -> Tuple:
"""simple docstring"""
a = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(__lowerCAmelCase , "positive" )
| 32
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 1
|
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :str , UpperCAmelCase__ :List[Any] ):
'''simple docstring'''
if gpta_config_file == "":
a = GPTaConfig()
else:
a = GPTaConfig.from_json_file(UpperCAmelCase__ )
a = GPTaModel(UpperCAmelCase__ )
# Load weights from numpy
load_tf_weights_in_gpta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
a = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
a = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
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__":
A_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow 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(
'''--gpt2_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
A_ : Optional[int] = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 1
|
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any]=13 , __lowerCAmelCase : Optional[int]=7 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : List[Any]=99 , __lowerCAmelCase : str=32 , __lowerCAmelCase : List[str]=5 , __lowerCAmelCase : List[str]=4 , __lowerCAmelCase : List[str]=37 , __lowerCAmelCase : int="gelu" , __lowerCAmelCase : Any=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Any=16 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : Dict=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : List[str]=4 , __lowerCAmelCase : List[Any]=None , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : int ) -> Dict:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str] ) -> Optional[Any]:
"""simple docstring"""
a = NystromformerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a = NystromformerForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = NystromformerForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
a = self.num_labels
a = NystromformerForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Tuple , __lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
a = self.num_labels
a = NystromformerForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = self.num_choices
a = NystromformerForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
a = NystromformerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> List[str]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def A ( self : str ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase )
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def A ( self : Any ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def A ( self : List[str] ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def A ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = NystromformerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
a = NystromformerModel.from_pretrained("uw-madison/nystromformer-512" )
a = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=1E-4 ) )
@slow
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = "the [MASK] of Belgium is Brussels"
a = AutoTokenizer.from_pretrained("uw-madison/nystromformer-512" )
a = NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512" )
a = tokenizer(__lowerCAmelCase , return_tensors="pt" )
with torch.no_grad():
a = model(encoding.input_ids ).logits
a = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(__lowerCAmelCase ) , "capital" )
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 1
|
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
A_ : List[Any] = logging.get_logger(__name__)
A_ : List[str] = '''T5Config'''
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''mt5'''
_UpperCAmelCase = MTaConfig
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''mt5'''
_UpperCAmelCase = MTaConfig
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''mt5'''
_UpperCAmelCase = MTaConfig
| 32
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 1
|
def UpperCAmelCase__ ( UpperCAmelCase__ :list[int] , UpperCAmelCase__ :list[int] ):
'''simple docstring'''
if not len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ) == 3:
raise ValueError("Please enter a valid equation." )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("Both a & b of two equations can't be zero." )
# Extract the coefficients
a , a , a = equationa
a , a , a = equationa
# Calculate the determinants of the matrices
a = aa * ba - aa * ba
a = ca * ba - ca * ba
a = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("Infinite solutions. (Consistent system)" )
else:
raise ValueError("No solution. (Inconsistent system)" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
a = determinant_x / determinant
a = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 32
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 1
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :Dict , UpperCAmelCase__ :Optional[int]=None , UpperCAmelCase__ :Tuple=None ):
'''simple docstring'''
if attention_mask is None:
a = tf.cast(tf.math.not_equal(UpperCAmelCase__ , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class _lowercase :
_UpperCAmelCase = OPTConfig
_UpperCAmelCase = {}
_UpperCAmelCase = '''gelu'''
def __init__( self : Optional[int] , __lowerCAmelCase : int , __lowerCAmelCase : str=13 , __lowerCAmelCase : int=7 , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : List[Any]=False , __lowerCAmelCase : Union[str, Any]=99 , __lowerCAmelCase : Optional[int]=16 , __lowerCAmelCase : Optional[Any]=2 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=4 , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : Tuple=20 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Dict=1 , __lowerCAmelCase : List[str]=0 , __lowerCAmelCase : str=16 , __lowerCAmelCase : Union[str, Any]=16 , ) -> Dict:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = eos_token_id
a = pad_token_id
a = bos_token_id
a = embed_dim
a = word_embed_proj_dim
a = False
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
a = tf.concat([input_ids, eos_tensor] , axis=1 )
a = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__lowerCAmelCase , **self.config_updates , )
a = prepare_opt_inputs_dict(__lowerCAmelCase , __lowerCAmelCase )
return config, inputs_dict
def A ( self : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
a = TFOPTModel(config=__lowerCAmelCase )
a = inputs_dict["input_ids"]
a = input_ids[:1, :]
a = inputs_dict["attention_mask"][:1, :]
a = 1
# first forward pass
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , use_cache=__lowerCAmelCase )
a , a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
a = ids_tensor((self.batch_size, 3) , config.vocab_size )
a = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
a = tf.concat([input_ids, next_tokens] , axis=-1 )
a = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase )[0]
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , past_key_values=__lowerCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
a = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
a = output_from_no_past[:, -3:, random_slice_idx]
a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__lowerCAmelCase , __lowerCAmelCase , rtol=1E-3 )
@require_tf
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
_UpperCAmelCase = (TFOPTForCausalLM,) if is_tf_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = 10
def A ( self : int ) -> List[str]:
"""simple docstring"""
a = TFOPTModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase )
def A ( self : str ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase )
def A ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__lowerCAmelCase : Dict , __lowerCAmelCase : Dict ):
if hasattr(__lowerCAmelCase , "weight" ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__lowerCAmelCase , "weight" ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
a = model_class(config=__lowerCAmelCase )
a = _get_word_embedding_weight(__lowerCAmelCase , model.get_input_embeddings() )
a = _get_word_embedding_weight(__lowerCAmelCase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__lowerCAmelCase )
a = _get_word_embedding_weight(__lowerCAmelCase , model.get_input_embeddings() )
a = _get_word_embedding_weight(__lowerCAmelCase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
a = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __lowerCAmelCase )
# check that weights remain the same after resizing
a = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
a = False
self.assertTrue(__lowerCAmelCase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __lowerCAmelCase )
a = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
a = False
self.assertTrue(__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] ):
'''simple docstring'''
return tf.constant(UpperCAmelCase__ , dtype=tf.intaa )
@require_tf
class _lowercase ( unittest.TestCase ):
_UpperCAmelCase = 99
def A ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
a = tf.ones((4, 1) , dtype=tf.intaa ) * 2
a = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
a = input_ids.shape[0]
a = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Any ) -> Tuple:
"""simple docstring"""
a = TFOPTModel.from_pretrained("facebook/opt-350m" )
a = _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
a = tf.not_equal(__lowerCAmelCase , model.config.pad_token_id )
with tf.GradientTape():
a = model(input_ids=__lowerCAmelCase , attention_mask=__lowerCAmelCase ).last_hidden_state
a = (1, 11, 512)
self.assertEqual(output.shape , __lowerCAmelCase )
a = tf.constant(
[[-0.2_8_7_3, -1.9_2_1_8, -0.3_0_3_3], [-1.2_7_1_0, -0.1_3_3_8, -0.1_9_0_2], [0.4_0_9_5, 0.1_2_1_4, -1.3_1_2_1]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=4E-3 ) )
a = tf.function(__lowerCAmelCase , jit_compile=__lowerCAmelCase )
a = xla_generate(__lowerCAmelCase , __lowerCAmelCase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=4E-2 ) )
@require_tf
@slow
class _lowercase ( unittest.TestCase ):
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
super().setUp()
a = "facebook/opt-350m"
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = TFOPTForCausalLM.from_pretrained(self.path_model )
a = GPTaTokenizer.from_pretrained(self.path_model )
a = [
"Today is a beautiful day and I want to",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
a = tokenizer(__lowerCAmelCase , return_tensors="tf" , padding=__lowerCAmelCase , add_special_tokens=__lowerCAmelCase )
a = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
a = tf.constant(
[
[1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
[-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
[0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
[6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
] )
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-4 ) )
a = tf.function(__lowerCAmelCase , jit_compile=__lowerCAmelCase )
a = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-4 ) )
@require_tf
@slow
class _lowercase ( unittest.TestCase ):
@property
def A ( self : Dict ) -> List[str]:
"""simple docstring"""
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def A ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
a = "facebook/opt-125m"
a = [
"Today is a beautiful day and I want to",
"In the city of New York, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
a = []
a = GPTaTokenizer.from_pretrained(__lowerCAmelCase )
a = TFOPTForCausalLM.from_pretrained(__lowerCAmelCase )
for prompt in self.prompts:
a = tokenizer(__lowerCAmelCase , return_tensors="tf" ).input_ids
a = model.generate(__lowerCAmelCase , max_length=10 )
a = tokenizer.batch_decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : Any ) -> Dict:
"""simple docstring"""
a = "facebook/opt-350m"
a = GPTaTokenizer.from_pretrained(__lowerCAmelCase )
a = TFOPTForCausalLM.from_pretrained(__lowerCAmelCase )
a = "left"
# use different length sentences to test batching
a = [
"Hello, my dog is a little",
"Today, I",
]
a = tokenizer(__lowerCAmelCase , return_tensors="tf" , padding=__lowerCAmelCase )
a = inputs["input_ids"]
a = model.generate(input_ids=__lowerCAmelCase , attention_mask=inputs["attention_mask"] )
a = tokenizer(sentences[0] , return_tensors="tf" ).input_ids
a = model.generate(input_ids=__lowerCAmelCase )
a = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs["attention_mask"][-1] , tf.intaa ) )
a = tokenizer(sentences[1] , return_tensors="tf" ).input_ids
a = model.generate(input_ids=__lowerCAmelCase , max_length=model.config.max_length - num_paddings )
a = tokenizer.batch_decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase )
a = tokenizer.decode(output_non_padded[0] , skip_special_tokens=__lowerCAmelCase )
a = tokenizer.decode(output_padded[0] , skip_special_tokens=__lowerCAmelCase )
a = [
"Hello, my dog is a little bit of a dork.\nI'm a little bit",
"Today, I was in the middle of a conversation with a friend about the",
]
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , [non_padded_sentence, padded_sentence] )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = "facebook/opt-350m"
a = [
"Today is a beautiful day and I want to",
"In the city of San Francisco, the city",
"Paris is the capital of France and the capital",
"Computers and mobile phones have taken over the",
]
a = []
a = GPTaTokenizer.from_pretrained(__lowerCAmelCase )
a = TFOPTForCausalLM.from_pretrained(__lowerCAmelCase )
for prompt in self.prompts:
a = tokenizer(__lowerCAmelCase , return_tensors="tf" ).input_ids
a = model.generate(__lowerCAmelCase , max_length=10 )
a = tokenizer.batch_decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase )
predicted_outputs += generated_string
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
| 32
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 1
|
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :Tuple , UpperCAmelCase__ :str ):
'''simple docstring'''
a = MobileBertConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = MobileBertForPreTraining(UpperCAmelCase__ )
# Load weights from tf checkpoint
a = load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--mobilebert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained MobileBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 32
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = (UniPCMultistepScheduler,)
_UpperCAmelCase = (('''num_inference_steps''', 25),)
def A ( self : List[Any] , **__lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__lowerCAmelCase )
return config
def A ( self : List[Any] , __lowerCAmelCase : Optional[int]=0 , **__lowerCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a , a = sample, sample
for t in range(__lowerCAmelCase , time_step + scheduler.config.solver_order + 1 ):
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any]=0 , **__lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__lowerCAmelCase )
a = scheduler_class.from_pretrained(__lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
a = dummy_past_residuals[: new_scheduler.config.solver_order]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = new_scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def A ( self : str , __lowerCAmelCase : Any=None , **__lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
if scheduler is None:
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = self.scheduler_classes[0]
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
return sample
def A ( self : Any ) -> int:
"""simple docstring"""
a = dict(self.forward_default_kwargs )
a = kwargs.pop("num_inference_steps" , __lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config()
a = scheduler_class(**__lowerCAmelCase )
a = self.dummy_sample
a = 0.1 * sample
if num_inference_steps is not None and hasattr(__lowerCAmelCase , "set_timesteps" ):
scheduler.set_timesteps(__lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(__lowerCAmelCase , "set_timesteps" ):
a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
a = dummy_past_residuals[: scheduler.config.solver_order]
a = scheduler.timesteps[5]
a = scheduler.timesteps[6]
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = UniPCMultistepScheduler(**self.get_scheduler_config() )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a = DEISMultistepScheduler.from_config(scheduler.config )
a = DPMSolverMultistepScheduler.from_config(scheduler.config )
a = UniPCMultistepScheduler.from_config(scheduler.config )
a = self.full_loop(scheduler=__lowerCAmelCase )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
self.check_over_configs(thresholding=__lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__lowerCAmelCase , prediction_type=__lowerCAmelCase , sample_max_value=__lowerCAmelCase , solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__lowerCAmelCase )
def A ( self : Optional[Any] ) -> Any:
"""simple docstring"""
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
a = self.full_loop(
solver_order=__lowerCAmelCase , solver_type=__lowerCAmelCase , prediction_type=__lowerCAmelCase , )
assert not torch.isnan(__lowerCAmelCase ).any(), "Samples have nan numbers"
def A ( self : Optional[int] ) -> Any:
"""simple docstring"""
self.check_over_configs(lower_order_final=__lowerCAmelCase )
self.check_over_configs(lower_order_final=__lowerCAmelCase )
def A ( self : Dict ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__lowerCAmelCase , time_step=0 )
def A ( self : Dict ) -> int:
"""simple docstring"""
a = self.full_loop()
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1E-3
def A ( self : Optional[int] ) -> int:
"""simple docstring"""
a = self.full_loop(prediction_type="v_prediction" )
a = torch.mean(torch.abs(__lowerCAmelCase ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1E-3
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a = self.scheduler_classes[0]
a = self.get_scheduler_config(thresholding=__lowerCAmelCase , dynamic_thresholding_ratio=0 )
a = scheduler_class(**__lowerCAmelCase )
a = 10
a = self.dummy_model()
a = self.dummy_sample_deter.half()
scheduler.set_timesteps(__lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
a = model(__lowerCAmelCase , __lowerCAmelCase )
a = scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
def A ( self : List[str] , **__lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
for scheduler_class in self.scheduler_classes:
a = self.get_scheduler_config(**__lowerCAmelCase )
a = scheduler_class(**__lowerCAmelCase )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 32
| 1
|
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str=768 ) -> int:
"""simple docstring"""
super().__init__(__lowerCAmelCase )
a = proj_size
a = CLIPVisionModel(__lowerCAmelCase )
a = PaintByExampleMapper(__lowerCAmelCase )
a = nn.LayerNorm(config.hidden_size )
a = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
a = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def A ( self : Any , __lowerCAmelCase : int , __lowerCAmelCase : Tuple=False ) -> List[str]:
"""simple docstring"""
a = self.model(pixel_values=__lowerCAmelCase )
a = clip_output.pooler_output
a = self.mapper(latent_states[:, None] )
a = self.final_layer_norm(__lowerCAmelCase )
a = self.proj_out(__lowerCAmelCase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class _lowercase ( nn.Module ):
def __init__( self : List[str] , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
super().__init__()
a = (config.num_hidden_layers + 1) // 5
a = config.hidden_size
a = 1
a = nn.ModuleList(
[
BasicTransformerBlock(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , activation_fn="gelu" , attention_bias=__lowerCAmelCase )
for _ in range(__lowerCAmelCase )
] )
def A ( self : Dict , __lowerCAmelCase : Any ) -> List[Any]:
"""simple docstring"""
for block in self.blocks:
a = block(__lowerCAmelCase )
return hidden_states
| 32
|
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 _lowercase :
def __init__( self : List[str] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int]=13 , __lowerCAmelCase : str=32 , __lowerCAmelCase : str=3 , __lowerCAmelCase : int=4 , __lowerCAmelCase : List[str]=[10, 20, 30, 40] , __lowerCAmelCase : Any=[2, 2, 3, 2] , __lowerCAmelCase : Any=True , __lowerCAmelCase : int=True , __lowerCAmelCase : str=37 , __lowerCAmelCase : List[Any]="gelu" , __lowerCAmelCase : int=10 , __lowerCAmelCase : str=0.0_2 , __lowerCAmelCase : int=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[str]=[2, 3, 4] , __lowerCAmelCase : str=None , ) -> Optional[Any]:
"""simple docstring"""
a = parent
a = batch_size
a = image_size
a = num_channels
a = num_stages
a = hidden_sizes
a = depths
a = is_training
a = use_labels
a = intermediate_size
a = hidden_act
a = num_labels
a = initializer_range
a = out_features
a = out_indices
a = scope
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.num_labels )
a = self.get_config()
return config, pixel_values, labels
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
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=__lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def A ( self : Any , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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
a = None
a = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase )
# 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 A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values}
return config, inputs_dict
def A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
a = self.prepare_config_and_inputs()
a , a , a = config_and_inputs
a = {"pixel_values": pixel_values, "labels": labels}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = ConvNextVaModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return
@unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
def A ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
def A ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
a , a = self.model_tester.prepare_config_and_inputs_with_labels()
a = False
a = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
a = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
a = model(**__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple ):
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
a = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
a = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
a = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , 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] , )
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None
@slow
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = preprocessor(images=__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(**__lowerCAmelCase )
# verify the logits
a = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
a = torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 32
| 1
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = StableDiffusionPanoramaPipeline
_UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def A ( self : Tuple ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
a = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
a = DDIMScheduler()
torch.manual_seed(0 )
a = 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 , )
torch.manual_seed(0 )
a = 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 , )
a = CLIPTextModel(__lowerCAmelCase )
a = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
a = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def A ( self : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict=0 ) -> Optional[int]:
"""simple docstring"""
a = torch.manual_seed(__lowerCAmelCase )
a = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def A ( self : Optional[int] ) -> str:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : str ) -> Dict:
"""simple docstring"""
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def A ( self : Any ) -> Optional[Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = "french fries"
a = sd_pipe(**__lowerCAmelCase , negative_prompt=__lowerCAmelCase )
a = output.images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : List[str] ) -> str:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase , view_batch_size=2 )
a = output.images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : str ) -> Any:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" )
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = "cpu" # ensure determinism for the device-dependent torch.Generator
a = self.get_dummy_components()
a = PNDMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , skip_prk_steps=__lowerCAmelCase )
a = StableDiffusionPanoramaPipeline(**__lowerCAmelCase )
a = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
a = self.get_dummy_inputs(__lowerCAmelCase )
a = sd_pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
a = np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Optional[Any] , __lowerCAmelCase : List[str]=0 ) -> Tuple:
"""simple docstring"""
a = torch.manual_seed(__lowerCAmelCase )
a = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = "stabilityai/stable-diffusion-2-base"
a = DDIMScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
a = self.get_inputs()
a = pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
a = np.array(
[
0.3_6_9_6_8_3_9_2,
0.2_7_0_2_5_3_7_2,
0.3_2_4_4_6_7_6_6,
0.2_8_3_7_9_3_8_7,
0.3_6_3_6_3_2_7_4,
0.3_0_7_3_3_3_4_7,
0.2_7_1_0_0_0_2_7,
0.2_7_0_5_4_1_2_5,
0.2_5_5_3_6_0_9_6,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base" , safety_checker=__lowerCAmelCase )
a = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
a = self.get_inputs()
a = pipe(**__lowerCAmelCase ).images
a = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
a = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def A ( self : Dict ) -> int:
"""simple docstring"""
a = 0
def callback_fn(__lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : torch.FloatTensor ) -> None:
a = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
a = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
a = latents[0, -3:, -3:, -1]
a = np.array(
[
0.1_8_6_8_1_8_6_9,
0.3_3_9_0_7_8_1_6,
0.5_3_6_1_2_7_6,
0.1_4_4_3_2_8_6_5,
-0.0_2_8_5_6_6_1_1,
-0.7_3_9_4_1_1_2_3,
0.2_3_3_9_7_9_8_7,
0.4_7_3_2_2_6_8_2,
-0.3_7_8_2_3_1_6_4,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
a = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
a = latents[0, -3:, -3:, -1]
a = np.array(
[
0.1_8_5_3_9_6_4_5,
0.3_3_9_8_7_2_4_8,
0.5_3_7_8_5_5_9,
0.1_4_4_3_7_1_4_2,
-0.0_2_4_5_5_2_6_1,
-0.7_3_3_8_3_1_7,
0.2_3_9_9_0_7_5_5,
0.4_7_3_5_6_2_7_2,
-0.3_7_8_6_5_0_5,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
a = False
a = "stabilityai/stable-diffusion-2-base"
a = DDIMScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
a = self.get_inputs()
pipe(**__lowerCAmelCase , callback=__lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def A ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
a = "stabilityai/stable-diffusion-2-base"
a = DDIMScheduler.from_pretrained(__lowerCAmelCase , subfolder="scheduler" )
a = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase , scheduler=__lowerCAmelCase , safety_checker=__lowerCAmelCase )
a = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
a = self.get_inputs()
a = pipe(**__lowerCAmelCase )
a = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 32
|
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _lowercase :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
a = ""
a = ""
a = []
a = 0
a = 256
a = 0
a = 0
a = 0
a = 0
def A ( self : Optional[Any] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
a = cva.imread(__lowerCAmelCase , 0 )
a = copy.deepcopy(self.img )
a , a , a = plt.hist(self.img.ravel() , 256 , [0, 256] , label="x" )
a = np.sum(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
a = x[i] / self.k
self.sk += prk
a = (self.L - 1) * self.sk
if self.rem != 0:
a = int(last % last )
a = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__lowerCAmelCase )
a = int(np.ma.count(self.img ) / self.img[1].size )
a = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a = self.img[j][i]
if num != self.last_list[num]:
a = self.last_list[num]
cva.imwrite("output_data/output.jpg" , self.img )
def A ( self : Any ) -> int:
"""simple docstring"""
plt.hist(self.img.ravel() , 256 , [0, 256] )
def A ( self : Any ) -> int:
"""simple docstring"""
cva.imshow("Output-Image" , self.img )
cva.imshow("Input-Image" , self.original_image )
cva.waitKey(5000 )
cva.destroyAllWindows()
if __name__ == "__main__":
A_ : List[Any] = os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
A_ : int = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image()
| 32
| 1
|
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
A_ : Tuple = threading.Lock()
A_ : Optional[logging.Handler] = None
A_ : List[Any] = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
A_ : Optional[Any] = logging.WARNING
A_ : List[Any] = True
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = os.getenv("TRANSFORMERS_VERBOSITY" , UpperCAmelCase__ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def UpperCAmelCase__ ( ):
'''simple docstring'''
return __name__.split("." )[0]
def UpperCAmelCase__ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
a = logging.StreamHandler() # Set sys.stderr as stream.
a = sys.stderr.flush
# Apply our default configuration to the library root logger.
a = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
a = False
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
a = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
a = None
def UpperCAmelCase__ ( ):
'''simple docstring'''
return log_levels
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[str] = None ):
'''simple docstring'''
if name is None:
a = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
return set_verbosity(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def UpperCAmelCase__ ( UpperCAmelCase__ :logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
a = False
def UpperCAmelCase__ ( ):
'''simple docstring'''
_configure_library_root_logger()
a = True
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = _get_library_root_logger().handlers
for handler in handlers:
a = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" )
handler.setFormatter(UpperCAmelCase__ )
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(UpperCAmelCase__ )
def UpperCAmelCase__ ( self :Any , *UpperCAmelCase__ :List[str] , **UpperCAmelCase__ :List[str] ):
'''simple docstring'''
a = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , UpperCAmelCase__ )
if no_advisory_warnings:
return
self.warning(*UpperCAmelCase__ , **UpperCAmelCase__ )
A_ : List[str] = warning_advice
@functools.lru_cache(UpperCAmelCase__ )
def UpperCAmelCase__ ( self :Dict , *UpperCAmelCase__ :int , **UpperCAmelCase__ :int ):
'''simple docstring'''
self.warning(*UpperCAmelCase__ , **UpperCAmelCase__ )
A_ : List[Any] = warning_once
class _lowercase :
def __init__( self : str , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : str ) -> Dict: # pylint: disable=unused-argument
"""simple docstring"""
a = args[0] if args else None
def __iter__( self : List[Any] ) -> Tuple:
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self : Dict , __lowerCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
def empty_fn(*__lowerCAmelCase : Optional[int] , **__lowerCAmelCase : Optional[int] ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : Tuple ) -> Optional[int]:
"""simple docstring"""
return self
def __exit__( self : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
return
class _lowercase :
def __call__( self : List[Any] , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm(*__lowerCAmelCase , **__lowerCAmelCase )
else:
return EmptyTqdm(*__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : Dict , *__lowerCAmelCase : Any , **__lowerCAmelCase : Dict ) -> Optional[int]:
"""simple docstring"""
a = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : List[Any] ) -> Tuple:
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
A_ : List[str] = _tqdm_cls()
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
a = True
hf_hub_utils.enable_progress_bars()
def UpperCAmelCase__ ( ):
'''simple docstring'''
global _tqdm_active
a = False
hf_hub_utils.disable_progress_bars()
| 32
|
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self : Optional[Any] , __lowerCAmelCase : UNetaDModel , __lowerCAmelCase : ScoreSdeVeScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
@torch.no_grad()
def __call__( self : int , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 2000 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , **__lowerCAmelCase : Any , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
a = self.unet.config.sample_size
a = (batch_size, 3, img_size, img_size)
a = self.unet
a = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase ) * self.scheduler.init_noise_sigma
a = sample.to(self.device )
self.scheduler.set_timesteps(__lowerCAmelCase )
self.scheduler.set_sigmas(__lowerCAmelCase )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
a = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
a = self.unet(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_correct(__lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase ).prev_sample
# prediction step
a = model(__lowerCAmelCase , __lowerCAmelCase ).sample
a = self.scheduler.step_pred(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , generator=__lowerCAmelCase )
a , a = output.prev_sample, output.prev_sample_mean
a = sample_mean.clamp(0 , 1 )
a = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
a = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 32
| 1
|
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
A_ : Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__ )
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : List[Any] , *__lowerCAmelCase : Optional[int] , **__lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
self.check_model_type(__lowerCAmelCase )
def A ( self : int , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : Optional[Any]=None , **__lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
a , a = {}, {}
if padding is not None:
a = padding
if truncation is not None:
a = truncation
if top_k is not None:
a = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : Optional[int] , __lowerCAmelCase : Union["Image.Image", str] , __lowerCAmelCase : str = None , **__lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
if isinstance(__lowerCAmelCase , (Image.Image, str) ) and isinstance(__lowerCAmelCase , __lowerCAmelCase ):
a = {"image": image, "question": question}
else:
a = image
a = super().__call__(__lowerCAmelCase , **__lowerCAmelCase )
return results
def A ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : Dict=False ) -> Tuple:
"""simple docstring"""
a = load_image(inputs["image"] )
a = self.tokenizer(
inputs["question"] , return_tensors=self.framework , padding=__lowerCAmelCase , truncation=__lowerCAmelCase )
a = self.image_processor(images=__lowerCAmelCase , return_tensors=self.framework )
model_inputs.update(__lowerCAmelCase )
return model_inputs
def A ( self : Union[str, Any] , __lowerCAmelCase : str ) -> Any:
"""simple docstring"""
a = self.model(**__lowerCAmelCase )
return model_outputs
def A ( self : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any]=5 ) -> Optional[Any]:
"""simple docstring"""
if top_k > self.model.config.num_labels:
a = self.model.config.num_labels
if self.framework == "pt":
a = model_outputs.logits.sigmoid()[0]
a , a = probs.topk(__lowerCAmelCase )
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
a = scores.tolist()
a = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCAmelCase , __lowerCAmelCase )]
| 32
|
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
assert len(str(UpperCAmelCase__ ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a = year // 1_00
a = (5 * (century % 4) + 2) % 7
a = year % 1_00
a = centurian % 12
a = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ : Tuple = logging.get_logger(__name__)
def UpperCAmelCase__ ( UpperCAmelCase__ :nn.ModuleList , UpperCAmelCase__ :nn.ModuleList , UpperCAmelCase__ :List[int] ):
'''simple docstring'''
a = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ), F"""{len(UpperCAmelCase__ )} != {len(UpperCAmelCase__ )}"""
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A_ : int = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A_ : Tuple = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :str ):
'''simple docstring'''
try:
a = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
F""" {n_student}""" )
return list(range(UpperCAmelCase__ ) )
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] , UpperCAmelCase__ :int ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}""" )
elif n_teacher == n_student:
return list(range(UpperCAmelCase__ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, PreTrainedModel] , UpperCAmelCase__ :Union[str, Path] = "student" , UpperCAmelCase__ :Union[int, None] = None , UpperCAmelCase__ :Union[int, None] = None , UpperCAmelCase__ :int=False , UpperCAmelCase__ :Dict=None , UpperCAmelCase__ :Optional[int]=None , **UpperCAmelCase__ :Any , ):
'''simple docstring'''
a = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
AutoTokenizer.from_pretrained(UpperCAmelCase__ ).save_pretrained(UpperCAmelCase__ ) # purely for convenience
a = AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__ ).eval()
else:
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ), F"""teacher must be a model or string got type {type(UpperCAmelCase__ )}"""
a = teacher.config.to_diff_dict()
try:
a , a = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
a = teacher_e
if d is None:
a = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
a , a = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
a , a = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
a = teacher_e
if d is None:
a = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(UpperCAmelCase__ )
# Copy weights
a = teacher.config_class(**UpperCAmelCase__ )
a = AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
a = student.load_state_dict(teacher.state_dict() , strict=UpperCAmelCase__ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
a , a = list(range(UpperCAmelCase__ ) ), list(range(UpperCAmelCase__ ) )
logger.info(
F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
F""" {save_path}""" )
student.save_pretrained(UpperCAmelCase__ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
a = pick_layers_to_copy(UpperCAmelCase__ , UpperCAmelCase__ )
if d_layers_to_copy is None:
a = pick_layers_to_copy(UpperCAmelCase__ , UpperCAmelCase__ )
try:
if hasattr(
UpperCAmelCase__ , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , UpperCAmelCase__ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , UpperCAmelCase__ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , UpperCAmelCase__ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , UpperCAmelCase__ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , UpperCAmelCase__ )
copy_layers(teacher.decoder.block , student.decoder.block , UpperCAmelCase__ )
logger.info(
F"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
a = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(UpperCAmelCase__ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 32
|
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
A_ : int = logging.getLogger(__name__)
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=UpperCAmelCase__, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class _lowercase :
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = field(
default=128, metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
}, )
_UpperCAmelCase = field(
default=UpperCAmelCase__, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase__ ( ):
'''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()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
" --overwrite_output_dir to overcome." )
a = import_module("tasks" )
try:
a = getattr(UpperCAmelCase__ , model_args.task_type )
a = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
a = token_classification_task.get_labels(data_args.labels )
a = dict(enumerate(UpperCAmelCase__ ) )
a = len(UpperCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , idalabel=UpperCAmelCase__ , labelaid={label: i for i, label in enumerate(UpperCAmelCase__ )} , cache_dir=model_args.cache_dir , )
a = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
a = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , )
# Get datasets
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
a = (
TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(UpperCAmelCase__ :np.ndarray , UpperCAmelCase__ :np.ndarray ) -> Tuple[List[int], List[int]]:
a = np.argmax(UpperCAmelCase__ , axis=2 )
a , a = preds.shape
a = [[] for _ in range(UpperCAmelCase__ )]
a = [[] for _ in range(UpperCAmelCase__ )]
for i in range(UpperCAmelCase__ ):
for j in range(UpperCAmelCase__ ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(UpperCAmelCase__ :EvalPrediction ) -> Dict:
a , a = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"precision": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"recall": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"f1": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
# Data collator
a = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
a = Trainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , compute_metrics=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a = trainer.evaluate()
a = os.path.join(training_args.output_dir , "eval_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
results.update(UpperCAmelCase__ )
# Predict
if training_args.do_predict:
a = TokenClassificationDataset(
token_classification_task=UpperCAmelCase__ , data_dir=data_args.data_dir , tokenizer=UpperCAmelCase__ , labels=UpperCAmelCase__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
a , a , a = trainer.predict(UpperCAmelCase__ )
a , a = align_predictions(UpperCAmelCase__ , UpperCAmelCase__ )
a = os.path.join(training_args.output_dir , "test_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
for key, value in metrics.items():
logger.info(" %s = %s" , UpperCAmelCase__ , UpperCAmelCase__ )
writer.write("%s = %s\n" % (key, value) )
# Save predictions
a = os.path.join(training_args.output_dir , "test_predictions.txt" )
if trainer.is_world_process_zero():
with open(UpperCAmelCase__ , "w" ) as writer:
with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f:
token_classification_task.write_predictions_to_file(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return results
def UpperCAmelCase__ ( UpperCAmelCase__ :Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 32
| 1
|
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCAmelCase__ ( UpperCAmelCase__ :Any , UpperCAmelCase__ :int ):
'''simple docstring'''
a = k_size // 2
a , a = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
a = 1 / (2 * pi * sigma) * exp(-(square(UpperCAmelCase__ ) + square(UpperCAmelCase__ )) / (2 * square(UpperCAmelCase__ )) )
return g
def UpperCAmelCase__ ( UpperCAmelCase__ :str , UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :Optional[Any] ):
'''simple docstring'''
a , a = image.shape[0], image.shape[1]
# dst image height and width
a = height - k_size + 1
a = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
a = zeros((dst_height * dst_width, k_size * k_size) )
a = 0
for i, j in product(range(UpperCAmelCase__ ) , range(UpperCAmelCase__ ) ):
a = ravel(image[i : i + k_size, j : j + k_size] )
a = window
row += 1
# turn the kernel into shape(k*k, 1)
a = gen_gaussian_kernel(UpperCAmelCase__ , UpperCAmelCase__ )
a = ravel(UpperCAmelCase__ )
# reshape and get the dst image
a = dot(UpperCAmelCase__ , UpperCAmelCase__ ).reshape(UpperCAmelCase__ , UpperCAmelCase__ ).astype(UpperCAmelCase__ )
return dst
if __name__ == "__main__":
# read original image
A_ : int = imread(r'''../image_data/lena.jpg''')
# turn image in gray scale value
A_ : str = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
A_ : List[str] = gaussian_filter(gray, 3, sigma=1)
A_ : Optional[int] = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
waitKey()
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : List[Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''rwkv'''
_UpperCAmelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCAmelCase : Union[str, Any]=5_0277 , __lowerCAmelCase : str=1024 , __lowerCAmelCase : Union[str, Any]=4096 , __lowerCAmelCase : Optional[int]=32 , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[Any]=1E-5 , __lowerCAmelCase : Union[str, Any]=0 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=False , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : List[str] , ) -> List[Any]:
"""simple docstring"""
a = vocab_size
a = context_length
a = hidden_size
a = num_hidden_layers
a = attention_hidden_size if attention_hidden_size is not None else hidden_size
a = intermediate_size if intermediate_size is not None else 4 * hidden_size
a = layer_norm_epsilon
a = rescale_every
a = use_cache
a = bos_token_id
a = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
| 1
|
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ : Any = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
def __init__( self : Dict , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : Union[str, Any] ) -> None:
"""simple docstring"""
warnings.warn(
"The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use LayoutLMv2ImageProcessor instead." , __lowerCAmelCase , )
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
| 32
|
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
A_ : List[str] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self : List[Any] , __lowerCAmelCase : Dict=2048 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=[16, 16] , __lowerCAmelCase : str=128 , __lowerCAmelCase : Optional[int]=4_4100 , __lowerCAmelCase : int=86 , __lowerCAmelCase : Optional[Any]=2048 , __lowerCAmelCase : str=0.0 , **__lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
feature_size=__lowerCAmelCase , sampling_rate=__lowerCAmelCase , padding_value=__lowerCAmelCase , **__lowerCAmelCase , )
a = spectrogram_length
a = num_channels
a = patch_size
a = feature_size // self.patch_size[1]
a = n_fft
a = sampling_rate // hop_length_to_sampling_rate
a = sampling_rate
a = padding_value
a = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCAmelCase , min_frequency=0.0 , max_frequency=2_2_0_5_0.0 , sampling_rate=__lowerCAmelCase , norm="slaney" , mel_scale="slaney" , ).T
def A ( self : List[str] , __lowerCAmelCase : np.array ) -> np.ndarray:
"""simple docstring"""
a = spectrogram(
__lowerCAmelCase , window_function(self.n_fft , "hann" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="dB" , db_range=8_0.0 , )
a = log_spec[:, :-1]
a = log_spec - 2_0.0
a = np.clip(log_spec / 4_0.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCAmelCase : Optional[Union[str, TensorType]] = None , __lowerCAmelCase : Optional[bool] = True , __lowerCAmelCase : Optional[int] = None , __lowerCAmelCase : bool = False , __lowerCAmelCase : bool = False , **__lowerCAmelCase : Optional[int] , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"This feature extractor is set to support sampling rate"
f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
f""" with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
a = isinstance(__lowerCAmelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
a = is_batched_numpy or (
isinstance(__lowerCAmelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
a = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCAmelCase , np.ndarray ):
a = np.asarray(__lowerCAmelCase , dtype=np.floataa )
elif isinstance(__lowerCAmelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
a = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
a = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
a = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , __lowerCAmelCase ):
a = [np.asarray(__lowerCAmelCase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
a = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
a = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
a = np.array(__lowerCAmelCase ).astype(np.floataa )
# convert into correct format for padding
a = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
a = np.ones([len(__lowerCAmelCase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
a = padded_audio_features * self.padding_value
for i in range(len(__lowerCAmelCase ) ):
a = audio_features[i]
a = feature
# return as BatchFeature
if return_attention_mask:
a = {"audio_values": padded_audio_features, "audio_mask": audio_mask}
else:
a = {"audio_values": padded_audio_features}
a = BatchFeature(data=__lowerCAmelCase , tensor_type=__lowerCAmelCase )
return encoded_inputs
| 32
| 1
|
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
A_ : Optional[int] = '''0.12''' # assumed parallelism: 8
if is_torch_available():
import torch
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :str , UpperCAmelCase__ :Any=None ):
'''simple docstring'''
if rng is None:
a = random.Random()
a = 1
for dim in shape:
total_dims *= dim
a = []
for _ in range(UpperCAmelCase__ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
a = np.array(UpperCAmelCase__ , dtype=jnp.intaa ).reshape(UpperCAmelCase__ )
return output
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :Dict=None ):
'''simple docstring'''
a = ids_tensor(UpperCAmelCase__ , vocab_size=2 , rng=UpperCAmelCase__ )
# make sure that at least one token is attended to for each batch
a = 1
return attn_mask
@require_flax
class _lowercase :
_UpperCAmelCase = None
_UpperCAmelCase = ()
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
a = 2
a = inputs["input_ids"].shape[-1] // 2
a = inputs["input_ids"][:max_batch_size, :sequence_length]
a = jnp.ones_like(__lowerCAmelCase )
a = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
a = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
a = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def A ( self : Dict ) -> List[Any]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
a = 0
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model_class.__name__[4:] # Skip the "Flax" at the beginning
a = getattr(__lowerCAmelCase , __lowerCAmelCase )
a = pt_model_class(__lowerCAmelCase ).eval()
a = load_flax_weights_in_pytorch_model(__lowerCAmelCase , flax_model.params )
a = flax_model.generate(__lowerCAmelCase ).sequences
a = pt_model.generate(torch.tensor(__lowerCAmelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
a = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Union[str, Any] ) -> str:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = True
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
a = 2
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = False
a = max_length
a = 2
a = 2
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def A ( self : str ) -> Any:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = True
a = max_length
a = 0.8
a = 10
a = 0.3
a = 1
a = 8
a = 9
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = max_length
a = 1
a = 8
a = 9
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : str ) -> List[str]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
a = max_length
a = 2
a = 1
a = 8
a = 9
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
# pad attention mask on the left
a = attention_mask.at[(0, 0)].set(0 )
a = False
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ) -> List[str]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
# pad attention mask on the left
a = attention_mask.at[(0, 0)].set(0 )
a = True
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Dict ) -> List[str]:
"""simple docstring"""
a , a , a , a = self._get_input_ids_and_config()
# pad attention mask on the left
a = attention_mask.at[(0, 0)].set(0 )
a = 2
a = max_length
for model_class in self.all_generative_model_classes:
a = model_class(__lowerCAmelCase )
a = model.generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , __lowerCAmelCase )
a = jit(model.generate )
a = jit_generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class _lowercase ( unittest.TestCase ):
def A ( self : Dict ) -> Dict:
"""simple docstring"""
a = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
a = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
a = "Hello world"
a = tokenizer(__lowerCAmelCase , return_tensors="np" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(__lowerCAmelCase , "do_samples" ):
model.generate(__lowerCAmelCase , do_samples=__lowerCAmelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(__lowerCAmelCase , "foo" ):
a = {"foo": "bar"}
model.generate(__lowerCAmelCase , **__lowerCAmelCase )
| 32
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class _lowercase :
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Tuple=2 , __lowerCAmelCase : Optional[int]=True , __lowerCAmelCase : Optional[int]=False , __lowerCAmelCase : int=10 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Optional[int]=32 * 4 , __lowerCAmelCase : Dict=32 * 6 , __lowerCAmelCase : str=4 , __lowerCAmelCase : Dict=32 , ) -> Any:
"""simple docstring"""
a = parent
a = batch_size
a = is_training
a = use_auxiliary_loss
a = num_queries
a = num_channels
a = min_size
a = max_size
a = num_labels
a = mask_feature_size
def A ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
a = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__lowerCAmelCase )
a = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__lowerCAmelCase )
a = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__lowerCAmelCase ) > 0.5
).float()
a = (torch.rand((self.batch_size, self.num_labels) , device=__lowerCAmelCase ) > 0.5).long()
a = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def A ( self : str ) -> Any:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def A ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
a , a , a , a , a = self.prepare_config_and_inputs()
a = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def A ( self : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
a = output.encoder_hidden_states
a = output.pixel_decoder_hidden_states
a = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__lowerCAmelCase ) , config.decoder_config.decoder_layers )
def A ( self : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
with torch.no_grad():
a = MaskFormerModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__lowerCAmelCase , __lowerCAmelCase )
def A ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Any , __lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
a = MaskFormerForInstanceSegmentation(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
def comm_check_on_output(__lowerCAmelCase : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a = model(pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase )
a = model(__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
a = model(
pixel_values=__lowerCAmelCase , pixel_mask=__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
comm_check_on_output(__lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = MaskFormerModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self : Any ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase )
@unittest.skip(reason="MaskFormer does not use inputs_embeds" )
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer is not a generative model" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="MaskFormer does not use token embeddings" )
def A ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
pass
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase )
a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a = [*signature.parameters.keys()]
a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
@slow
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
a = MaskFormerModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def A ( self : str ) -> Dict:
"""simple docstring"""
a = (self.model_tester.min_size,) * 2
a = {
"pixel_values": torch.randn((2, 3, *size) , device=__lowerCAmelCase ),
"mask_labels": torch.randn((2, 10, *size) , device=__lowerCAmelCase ),
"class_labels": torch.zeros(2 , 10 , device=__lowerCAmelCase ).long(),
}
a = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def A ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase , **__lowerCAmelCase , output_hidden_states=__lowerCAmelCase )
def A ( self : List[str] ) -> Any:
"""simple docstring"""
a , a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a = model_class(__lowerCAmelCase ).to(__lowerCAmelCase )
a = model(**__lowerCAmelCase , output_attentions=__lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def A ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase ).loss
loss.backward()
def A ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a = self.all_model_classes[1]
a , a , a , a , a = self.model_tester.prepare_config_and_inputs()
a = True
a = True
a = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
a = model(__lowerCAmelCase , mask_labels=__lowerCAmelCase , class_labels=__lowerCAmelCase )
a = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
a = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : int = 1E-4
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : int ) -> Optional[int]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco" )
if is_vision_available()
else None
)
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(__lowerCAmelCase )
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
a = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
a = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(__lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : str ) -> Union[str, Any]:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = prepare_img()
a = image_processor(__lowerCAmelCase , return_tensors="pt" ).to(__lowerCAmelCase )
a = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
a = model(**__lowerCAmelCase )
# masks_queries_logits
a = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
a = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
a = torch.tensor(__lowerCAmelCase ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
# class_queries_logits
a = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
a = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase ) )
def A ( self : int ) -> Any:
"""simple docstring"""
a = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
.to(__lowerCAmelCase )
.eval()
)
a = self.default_image_processor
a = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a = inputs["pixel_values"].to(__lowerCAmelCase )
a = [el.to(__lowerCAmelCase ) for el in inputs["mask_labels"]]
a = [el.to(__lowerCAmelCase ) for el in inputs["class_labels"]]
with torch.no_grad():
a = model(**__lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 32
| 1
|
A_ : str = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCAmelCase__ ( UpperCAmelCase__ :float ):
'''simple docstring'''
assert type(UpperCAmelCase__ ) in (int, float) and decimal == int(UpperCAmelCase__ )
a = int(UpperCAmelCase__ )
a = ""
a = False
if decimal < 0:
a = True
decimal *= -1
while decimal > 0:
a , a = divmod(UpperCAmelCase__ , 16 )
a = values[remainder] + hexadecimal
a = "0x" + hexadecimal
if negative:
a = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _lowercase ( unittest.TestCase ):
def A ( self : Union[str, Any] ) -> int:
"""simple docstring"""
a = [[1, 2, 4], [1, 2, 3, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
self.assertTrue(isinstance(dc.token_ids , __lowerCAmelCase ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def A ( self : Tuple ) -> Dict:
"""simple docstring"""
a = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__lowerCAmelCase ):
DisjunctiveConstraint(__lowerCAmelCase ) # fails here
def A ( self : int ) -> Any:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
a = stepped is True and completed is False and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(3 )
a = stepped is True and completed is True and reset is False
self.assertTrue(__lowerCAmelCase )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def A ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
a = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
a = DisjunctiveConstraint(__lowerCAmelCase )
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
a , a , a = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
a , a , a = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
a , a , a = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 32
| 1
|
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=UpperCAmelCase__ ):
_UpperCAmelCase = ['''torch''', '''scipy''']
def __init__( self : Tuple , *__lowerCAmelCase : Tuple , **__lowerCAmelCase : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["torch", "scipy"] )
@classmethod
def A ( cls : Union[str, Any] , *__lowerCAmelCase : str , **__lowerCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "scipy"] )
@classmethod
def A ( cls : List[Any] , *__lowerCAmelCase : Any , **__lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
requires_backends(cls , ["torch", "scipy"] )
| 32
|
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ :int ):
'''simple docstring'''
a = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == set("123456789" )
def UpperCAmelCase__ ( ):
'''simple docstring'''
for base_num in range(99_99 , 49_99 , -1 ):
a = 10_00_02 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
a = 1_00_20_03 * base_num
if is_9_pandigital(UpperCAmelCase__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
| 1
|
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Dict ) -> Any:
"""simple docstring"""
a = tempfile.mkdtemp()
a = 5
# Realm tok
a = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"test",
"question",
"this",
"is",
"the",
"first",
"second",
"third",
"fourth",
"fifth",
"record",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
a = os.path.join(self.tmpdirname , "realm_tokenizer" )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
a = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
a = os.path.join(self.tmpdirname , "realm_block_records" )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
def A ( self : List[str] ) -> RealmTokenizer:
"""simple docstring"""
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , "realm_tokenizer" ) )
def A ( self : Dict ) -> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A ( self : List[str] ) -> List[str]:
"""simple docstring"""
a = RealmConfig(num_block_records=self.num_block_records )
return config
def A ( self : str ) -> Optional[Any]:
"""simple docstring"""
a = Dataset.from_dict(
{
"id": ["0", "1"],
"question": ["foo", "bar"],
"answers": [["Foo", "Bar"], ["Bar"]],
} )
return dataset
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = np.array(
[
B"This is the first record",
B"This is the second record",
B"This is the third record",
B"This is the fourth record",
B"This is the fifth record",
B"This is a longer longer longer record",
] , dtype=__lowerCAmelCase , )
return block_records
def A ( self : Any ) -> Dict:
"""simple docstring"""
a = RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def A ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
a = self.get_config()
a = self.get_dummy_retriever()
a = retriever.tokenizer
a = np.array([0, 3] , dtype="long" )
a = tokenizer(["Test question"] ).input_ids
a = tokenizer(
["the fourth"] , add_special_tokens=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , ).input_ids
a = config.reader_seq_len
a , a , a , a = retriever(
__lowerCAmelCase , __lowerCAmelCase , answer_ids=__lowerCAmelCase , max_length=__lowerCAmelCase , return_tensors="np" )
self.assertEqual(len(__lowerCAmelCase ) , 2 )
self.assertEqual(len(__lowerCAmelCase ) , 2 )
self.assertEqual(len(__lowerCAmelCase ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 10) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 10) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 10) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"] , )
def A ( self : int ) -> int:
"""simple docstring"""
a = self.get_config()
a = self.get_dummy_retriever()
a = retriever.tokenizer
a = np.array([0, 3, 5] , dtype="long" )
a = tokenizer(["Test question"] ).input_ids
a = tokenizer(
["the fourth", "longer longer"] , add_special_tokens=__lowerCAmelCase , return_token_type_ids=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , ).input_ids
a = config.reader_seq_len
a , a , a , a = retriever(
__lowerCAmelCase , __lowerCAmelCase , answer_ids=__lowerCAmelCase , max_length=__lowerCAmelCase , return_tensors="np" )
self.assertEqual([False, True, True] , __lowerCAmelCase )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , __lowerCAmelCase )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , __lowerCAmelCase )
def A ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
a = self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , "realm_block_records" ) )
# Test local path
a = retriever.from_pretrained(os.path.join(self.tmpdirname , "realm_block_records" ) )
self.assertEqual(retriever.block_records[0] , B"This is the first record" )
# Test mocked remote path
with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download:
a = os.path.join(
os.path.join(self.tmpdirname , "realm_block_records" ) , _REALM_BLOCK_RECORDS_FILENAME )
a = RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" )
self.assertEqual(retriever.block_records[0] , B"This is the first record" )
| 32
|
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__ ), '''Tatoeba directory does not exist.''' )
class _lowercase ( unittest.TestCase ):
@cached_property
def A ( self : List[str] ) -> int:
"""simple docstring"""
a = tempfile.mkdtemp()
return TatoebaConverter(save_dir=__lowerCAmelCase )
@slow
def A ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
self.resolver.convert_models(["heb-eng"] )
@slow
def A ( self : Dict ) -> Any:
"""simple docstring"""
a , a = self.resolver.write_model_card("opus-mt-he-en" , dry_run=__lowerCAmelCase )
assert mmeta["long_pair"] == "heb-eng"
| 32
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : str = {
'''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
'''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = ['''BertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Dict = [
'''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BertForMaskedLM''',
'''BertForMultipleChoice''',
'''BertForNextSentencePrediction''',
'''BertForPreTraining''',
'''BertForQuestionAnswering''',
'''BertForSequenceClassification''',
'''BertForTokenClassification''',
'''BertLayer''',
'''BertLMHeadModel''',
'''BertModel''',
'''BertPreTrainedModel''',
'''load_tf_weights_in_bert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = [
'''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBertEmbeddings''',
'''TFBertForMaskedLM''',
'''TFBertForMultipleChoice''',
'''TFBertForNextSentencePrediction''',
'''TFBertForPreTraining''',
'''TFBertForQuestionAnswering''',
'''TFBertForSequenceClassification''',
'''TFBertForTokenClassification''',
'''TFBertLMHeadModel''',
'''TFBertMainLayer''',
'''TFBertModel''',
'''TFBertPreTrainedModel''',
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : int = ['''TFBertTokenizer''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Tuple = [
'''FlaxBertForCausalLM''',
'''FlaxBertForMaskedLM''',
'''FlaxBertForMultipleChoice''',
'''FlaxBertForNextSentencePrediction''',
'''FlaxBertForPreTraining''',
'''FlaxBertForQuestionAnswering''',
'''FlaxBertForSequenceClassification''',
'''FlaxBertForTokenClassification''',
'''FlaxBertModel''',
'''FlaxBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
),
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''lilt'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : Optional[Any]=3_0522 , __lowerCAmelCase : str=768 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : Optional[Any]=12 , __lowerCAmelCase : List[Any]=3072 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : Optional[int]=2 , __lowerCAmelCase : int=0.0_2 , __lowerCAmelCase : Union[str, Any]=1E-12 , __lowerCAmelCase : Tuple=0 , __lowerCAmelCase : List[Any]="absolute" , __lowerCAmelCase : int=None , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=1024 , **__lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = hidden_act
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = initializer_range
a = layer_norm_eps
a = position_embedding_type
a = classifier_dropout
a = channel_shrink_ratio
a = max_ad_position_embeddings
| 32
| 1
|
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
A_ : Optional[int] = logging.getLogger()
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, Any] ):
'''simple docstring'''
a = {}
a = os.path.join(UpperCAmelCase__ , "all_results.json" )
if os.path.exists(UpperCAmelCase__ ):
with open(UpperCAmelCase__ , "r" ) as f:
a = json.load(UpperCAmelCase__ )
else:
raise ValueError(F"""can't find {path}""" )
return results
A_ : Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Any ) -> Tuple:
"""simple docstring"""
import xla_spawn
a = self.get_auto_remove_tmp_dir()
a = f"""
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(__lowerCAmelCase , "argv" , __lowerCAmelCase ):
a = time()
xla_spawn.main()
a = time()
a = get_results(__lowerCAmelCase )
self.assertGreaterEqual(result["eval_accuracy"] , 0.7_5 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def A ( self : Tuple ) -> Tuple:
"""simple docstring"""
import xla_spawn
a = "\n ./tests/test_trainer_tpu.py\n --num_cores=8\n ./tests/test_trainer_tpu.py\n ".split()
with patch.object(__lowerCAmelCase , "argv" , __lowerCAmelCase ):
xla_spawn.main()
| 32
|
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase__ ( UpperCAmelCase__ :Optional[int] , UpperCAmelCase__ :List[str] , UpperCAmelCase__ :Any ):
'''simple docstring'''
a = TaConfig.from_json_file(UpperCAmelCase__ )
print(F"""Building PyTorch model from configuration: {config}""" )
a = TaForConditionalGeneration(UpperCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_ta(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
A_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A_ : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 32
| 1
|
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def UpperCAmelCase__ ( ):
'''simple docstring'''
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
a = "__test_patch_submodule_mock__"
with patch_submodule(_test_patching , "os.path.join" , UpperCAmelCase__ ):
# 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 UpperCAmelCase__ ( ):
'''simple docstring'''
assert _test_patching.open is open
a = "__test_patch_submodule_builtin_mock__"
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , "open" , UpperCAmelCase__ ):
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 UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_missing_mock__"
with patch_submodule(_test_patching , "pandas.read_csv" , UpperCAmelCase__ ):
pass
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_missing_builtin_mock__"
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , "len" , UpperCAmelCase__ ) is None
with patch_submodule(_test_patching , "len" , UpperCAmelCase__ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_start_and_stop_mock__"
a = patch_submodule(_test_patching , "open" , UpperCAmelCase__ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def UpperCAmelCase__ ( ):
'''simple docstring'''
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
a = "__test_patch_submodule_successive_join__"
a = "__test_patch_submodule_successive_dirname__"
a = "__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" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.rename" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.path.dirname" , UpperCAmelCase__ ):
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" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.path.join" , UpperCAmelCase__ ):
with patch_submodule(_test_patching , "os.path.dirname" , UpperCAmelCase__ ):
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 UpperCAmelCase__ ( ):
'''simple docstring'''
a = "__test_patch_submodule_doesnt_exist_mock__"
with patch_submodule(_test_patching , "__module_that_doesn_exist__.__attribute_that_doesn_exist__" , UpperCAmelCase__ ):
pass
with patch_submodule(_test_patching , "os.__attribute_that_doesn_exist__" , UpperCAmelCase__ ):
pass
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
a = max(len(UpperCAmelCase__ ) , len(UpperCAmelCase__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__ ) , b_binary.zfill(UpperCAmelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
| 1
|
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Any = {'''vocab_file''': '''spiece.model'''}
A_ : Optional[int] = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
A_ : List[str] = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
A_ : str = '''▁'''
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Any="<unk>" , __lowerCAmelCase : Dict="<pad>" , __lowerCAmelCase : int=100 , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : Optional[Dict[str, Any]] = None , __lowerCAmelCase : Tuple=True , **__lowerCAmelCase : Tuple , ) -> None:
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
a = [f"""<extra_id_{i}>""" for i in range(__lowerCAmelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
a = len(set(filter(lambda __lowerCAmelCase : bool("extra_id" in str(__lowerCAmelCase ) ) , __lowerCAmelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens" )
if legacy:
logger.warning_once(
f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
" read the related pull request available at https://github.com/huggingface/transformers/pull/24565" )
a = legacy
a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , extra_ids=__lowerCAmelCase , additional_special_tokens=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=__lowerCAmelCase , **__lowerCAmelCase , )
a = vocab_file
a = extra_ids
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowerCAmelCase )
@staticmethod
def A ( __lowerCAmelCase : str , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[int] ) -> Any:
"""simple docstring"""
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
a = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
f""" {pretrained_model_name_or_path} automatically truncating your input to"""
f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , __lowerCAmelCase , )
return max_model_length
@property
def A ( self : int ) -> Union[str, Any]:
"""simple docstring"""
return self.sp_model.get_piece_size() + self._extra_ids
def A ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A ( self : Tuple , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(__lowerCAmelCase )) + [1]
return ([0] * len(__lowerCAmelCase )) + [1] + ([0] * len(__lowerCAmelCase )) + [1]
def A ( self : Dict ) -> int:
"""simple docstring"""
return list(
set(filter(lambda __lowerCAmelCase : bool(re.search(R"<extra_id_\d+>" , __lowerCAmelCase ) ) is not None , self.additional_special_tokens ) ) )
def A ( self : str ) -> List[str]:
"""simple docstring"""
return [self._convert_token_to_id(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]
def A ( self : Union[str, Any] , __lowerCAmelCase : List[int] ) -> List[int]:
"""simple docstring"""
if len(__lowerCAmelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def A ( self : Dict , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
a = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def A ( self : List[str] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
a = self._add_eos_if_not_present(__lowerCAmelCase )
if token_ids_a is None:
return token_ids_a
else:
a = self._add_eos_if_not_present(__lowerCAmelCase )
return token_ids_a + token_ids_a
def __getstate__( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.__dict__.copy()
a = None
return state
def __setstate__( self : str , __lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
a = {}
a = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A ( self : Any , __lowerCAmelCase : "TextInput" , **__lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
if not self.legacy:
a = SPIECE_UNDERLINE + text.replace(__lowerCAmelCase , " " )
return super().tokenize(__lowerCAmelCase , **__lowerCAmelCase )
def A ( self : List[Any] , __lowerCAmelCase : Tuple , **__lowerCAmelCase : Dict ) -> Dict:
"""simple docstring"""
if not self.legacy:
a = text.startswith(__lowerCAmelCase )
if is_first:
a = text[1:]
a = self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(__lowerCAmelCase ):
a = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def A ( self : List[str] , __lowerCAmelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
if token.startswith("<extra_id_" ):
a = re.match(R"<extra_id_(\d+)>" , __lowerCAmelCase )
a = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(__lowerCAmelCase )
def A ( self : List[Any] , __lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
if index < self.sp_model.get_piece_size():
a = self.sp_model.IdToPiece(__lowerCAmelCase )
else:
a = f"""<extra_id_{self.vocab_size - 1 - index}>"""
return token
def A ( self : Dict , __lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
a = []
a = ""
a = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__lowerCAmelCase ) + token
a = True
a = []
else:
current_sub_tokens.append(__lowerCAmelCase )
a = False
out_string += self.sp_model.decode(__lowerCAmelCase )
return out_string.strip()
def A ( self : Dict , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
a = os.path.join(
__lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , "wb" ) as fi:
a = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
return (out_vocab_file,)
| 32
|
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
A_ : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
A_ : Optional[int] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _lowercase :
_UpperCAmelCase = 42
_UpperCAmelCase = 42
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Iterable[int] ) -> None:
"""simple docstring"""
a = None
for i in sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ):
a = Node(__lowerCAmelCase , self.head )
def __iter__( self : Union[str, Any] ) -> Iterator[int]:
"""simple docstring"""
a = self.head
while node:
yield node.data
a = node.next_node
def __len__( self : Tuple ) -> int:
"""simple docstring"""
return sum(1 for _ in self )
def __str__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return " -> ".join([str(__lowerCAmelCase ) for node in self] )
def UpperCAmelCase__ ( UpperCAmelCase__ :SortedLinkedList , UpperCAmelCase__ :SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCAmelCase__ ) + list(UpperCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 32
| 1
|
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from transformers import (
BertTokenizer,
BlipConfig,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
)
def UpperCAmelCase__ ( UpperCAmelCase__ :List[str] , UpperCAmelCase__ :str ):
'''simple docstring'''
a = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
a = Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__ ).raw ).convert("RGB" )
a = transforms.Compose(
[
transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073) , (0.26862954, 0.26130258, 0.27577711) ),
] )
a = transform(UpperCAmelCase__ ).unsqueeze(0 ).to(UpperCAmelCase__ )
return image
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
if "visual_encoder" in key:
a = re.sub("visual_encoder*" , "vision_model.encoder" , UpperCAmelCase__ )
if "blocks" in key:
a = re.sub(r"blocks" , "layers" , UpperCAmelCase__ )
if "attn" in key:
a = re.sub(r"attn" , "self_attn" , UpperCAmelCase__ )
if "norm1" in key:
a = re.sub(r"norm1" , "layer_norm1" , UpperCAmelCase__ )
if "norm2" in key:
a = re.sub(r"norm2" , "layer_norm2" , UpperCAmelCase__ )
if "encoder.norm" in key:
a = re.sub(r"encoder.norm" , "post_layernorm" , UpperCAmelCase__ )
if "encoder.patch_embed.proj" in key:
a = re.sub(r"encoder.patch_embed.proj" , "embeddings.patch_embedding" , UpperCAmelCase__ )
if "encoder.pos_embed" in key:
a = re.sub(r"encoder.pos_embed" , "embeddings.position_embedding" , UpperCAmelCase__ )
if "encoder.cls_token" in key:
a = re.sub(r"encoder.cls_token" , "embeddings.class_embedding" , UpperCAmelCase__ )
if "self_attn" in key:
a = re.sub(r"self_attn.proj" , "self_attn.projection" , UpperCAmelCase__ )
return key
@torch.no_grad()
def UpperCAmelCase__ ( UpperCAmelCase__ :int , UpperCAmelCase__ :List[str]=None ):
'''simple docstring'''
if config_path is not None:
a = BlipConfig.from_pretrained(UpperCAmelCase__ )
else:
a = BlipConfig(projection_dim=5_12 , text_config={} , vision_config={} )
a = BlipForConditionalGeneration(UpperCAmelCase__ ).eval()
a = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"
a = blip_decoder(pretrained=UpperCAmelCase__ , image_size=3_84 , vit="base" )
a = pt_model.eval()
a = pt_model.state_dict()
for key in modified_state_dict.copy():
a = modified_state_dict.pop(UpperCAmelCase__ )
a = rename_key(UpperCAmelCase__ )
a = value
hf_model.load_state_dict(UpperCAmelCase__ )
a = 3_84
a = load_demo_image(image_size=UpperCAmelCase__ , device="cpu" )
a = BertTokenizer.from_pretrained("bert-base-uncased" )
a = tokenizer(["a picture of"] ).input_ids
a = hf_model.generate(UpperCAmelCase__ , UpperCAmelCase__ )
assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
a = hf_model.generate(UpperCAmelCase__ )
assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]
if pytorch_dump_folder_path is not None:
hf_model.save_pretrained(UpperCAmelCase__ )
# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
a = (
"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
)
a = blip_vqa(pretrained=UpperCAmelCase__ , image_size=UpperCAmelCase__ , vit="base" )
vqa_model.eval()
a = vqa_model.state_dict()
for key in modified_state_dict.copy():
a = modified_state_dict.pop(UpperCAmelCase__ )
a = rename_key(UpperCAmelCase__ )
a = value
a = BlipForQuestionAnswering(UpperCAmelCase__ )
hf_vqa_model.load_state_dict(UpperCAmelCase__ )
a = ["How many dogs are in this image?"]
a = tokenizer(UpperCAmelCase__ , return_tensors="pt" ).input_ids
a = hf_vqa_model.generate(UpperCAmelCase__ , UpperCAmelCase__ )
print(tokenizer.decode(answer[0] ) )
assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]"
if pytorch_dump_folder_path is not None:
hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa" )
a = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"
a = blip_itm(pretrained=UpperCAmelCase__ , image_size=UpperCAmelCase__ , vit="base" )
itm_model.eval()
a = itm_model.state_dict()
for key in modified_state_dict.copy():
a = modified_state_dict.pop(UpperCAmelCase__ )
a = rename_key(UpperCAmelCase__ )
a = value
a = BlipForImageTextRetrieval(UpperCAmelCase__ )
a = ["A picture of a woman with a dog sitting in a beach"]
a = tokenizer(
UpperCAmelCase__ , return_tensors="pt" , padding="max_length" , truncation=UpperCAmelCase__ , max_length=35 , ).input_ids
hf_itm_model.load_state_dict(UpperCAmelCase__ )
hf_itm_model.eval()
a = hf_itm_model(UpperCAmelCase__ , UpperCAmelCase__ , use_itm_head=UpperCAmelCase__ )
a = hf_itm_model(UpperCAmelCase__ , UpperCAmelCase__ , use_itm_head=UpperCAmelCase__ )
assert out[0].item() == 0.2110687494277954
assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.45698845386505127
if pytorch_dump_folder_path is not None:
hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm" )
if __name__ == "__main__":
A_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
A_ : Any = parser.parse_args()
convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 32
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 32
| 1
|
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any]=13 , __lowerCAmelCase : Optional[Any]=7 , __lowerCAmelCase : Any=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Dict=True , __lowerCAmelCase : Any=99 , __lowerCAmelCase : Optional[Any]=24 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : Dict=6 , __lowerCAmelCase : int=37 , __lowerCAmelCase : Any="gelu" , __lowerCAmelCase : int=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Optional[Any]=512 , __lowerCAmelCase : Any=16 , __lowerCAmelCase : Optional[Any]=2 , __lowerCAmelCase : Optional[int]=0.0_2 , __lowerCAmelCase : List[str]=3 , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : str=1000 , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = scope
a = range_bbox
def A ( self : Optional[Any] ) -> int:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
a = bbox[i, j, 3]
a = bbox[i, j, 1]
a = t
if bbox[i, j, 2] < bbox[i, j, 0]:
a = bbox[i, j, 2]
a = bbox[i, j, 0]
a = t
a = None
if self.use_input_mask:
a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def A ( self : Dict , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int , ) -> Optional[Any]:
"""simple docstring"""
a = LiltModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , bbox=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , bbox=__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = LiltForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : int , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : str , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
a = LiltForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , bbox=__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : Any ) -> Any:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': LiltModel,
'''question-answering''': LiltForQuestionAnswering,
'''text-classification''': LiltForSequenceClassification,
'''token-classification''': LiltForTokenClassification,
'''zero-shot''': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
def A ( self : int , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : int ) -> int:
"""simple docstring"""
return True
def A ( self : int ) -> int:
"""simple docstring"""
a = LiltModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : str ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Any:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a = type
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self : List[Any] ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
def A ( self : Tuple ) -> List[Any]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
@slow
def A ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a = LiltModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
@require_torch
@slow
class _lowercase ( unittest.TestCase ):
def A ( self : int ) -> int:
"""simple docstring"""
a = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(__lowerCAmelCase )
a = torch.tensor([[1, 2]] , device=__lowerCAmelCase )
a = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=__lowerCAmelCase )
# forward pass
with torch.no_grad():
a = model(input_ids=__lowerCAmelCase , bbox=__lowerCAmelCase )
a = torch.Size([1, 2, 768] )
a = torch.tensor(
[[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]] , device=__lowerCAmelCase , )
self.assertTrue(outputs.last_hidden_state.shape , __lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , __lowerCAmelCase , atol=1E-3 ) )
| 32
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : int = logging.get_logger(__name__)
A_ : str = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__ ):
_UpperCAmelCase = '''focalnet'''
def __init__( self : int , __lowerCAmelCase : Optional[Any]=224 , __lowerCAmelCase : Any=4 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Tuple=96 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Optional[int]=[192, 384, 768, 768] , __lowerCAmelCase : Union[str, Any]=[2, 2, 6, 2] , __lowerCAmelCase : Optional[int]=[2, 2, 2, 2] , __lowerCAmelCase : Union[str, Any]=[3, 3, 3, 3] , __lowerCAmelCase : str="gelu" , __lowerCAmelCase : Any=4.0 , __lowerCAmelCase : Optional[int]=0.0 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : str=False , __lowerCAmelCase : Optional[int]=1E-4 , __lowerCAmelCase : Optional[Any]=False , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=False , __lowerCAmelCase : Any=0.0_2 , __lowerCAmelCase : str=1E-5 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=None , __lowerCAmelCase : str=None , **__lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
a = image_size
a = patch_size
a = num_channels
a = embed_dim
a = use_conv_embed
a = hidden_sizes
a = depths
a = focal_levels
a = focal_windows
a = hidden_act
a = mlp_ratio
a = hidden_dropout_prob
a = drop_path_rate
a = use_layerscale
a = layerscale_value
a = use_post_layernorm
a = use_post_layernorm_in_modulation
a = normalize_modulator
a = initializer_range
a = layer_norm_eps
a = encoder_stride
a = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(self.depths ) + 1 )]
a , a = get_aligned_output_features_output_indices(
out_features=__lowerCAmelCase , out_indices=__lowerCAmelCase , stage_names=self.stage_names )
| 32
| 1
|
import os
import pytest
from transformers.dynamic_module_utils import get_imports
A_ : Tuple = '''
import os
'''
A_ : str = '''
def foo():
import os
return False
'''
A_ : Optional[int] = '''
def foo():
def bar():
if True:
import os
return False
return bar()
'''
A_ : Optional[int] = '''
import os
try:
import bar
except ImportError:
raise ValueError()
'''
A_ : Union[str, Any] = '''
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
'''
A_ : str = '''
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
'''
A_ : Tuple = '''
import os
try:
import bar
except ImportError as e:
raise ValueError()
'''
A_ : Optional[int] = '''
import os
try:
import bar
except:
raise ValueError()
'''
A_ : Union[str, Any] = '''
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
'''
A_ : Union[str, Any] = '''
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
'''
A_ : Optional[int] = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize("case" , UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict , UpperCAmelCase__ :Tuple ):
'''simple docstring'''
a = os.path.join(UpperCAmelCase__ , "test_file.py" )
with open(UpperCAmelCase__ , "w" ) as _tmp_file:
_tmp_file.write(UpperCAmelCase__ )
a = get_imports(UpperCAmelCase__ )
assert parsed_imports == ["os"]
| 32
|
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
a , a = head.next, head
while fast and fast.next:
a = fast.next.next
a = slow.next
a = slow.next
a = None # Don't forget here! But forget still works!
# reverse the second part
a = None
while second:
a = second.next
a = node
a = second
a = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
a = node.next
a = head.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
a = a = a = head
while fast and fast.next:
a , a = fast.next.next, slow.next
# 2. Push the second half into the stack
a = [slow.val]
while slow.next:
a = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
a = cur.next
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :Any ):
'''simple docstring'''
if not head or not head.next:
return True
a = {}
a = 0
while head:
if head.val in d:
d[head.val].append(UpperCAmelCase__ )
else:
a = [pos]
a = head.next
pos += 1
a = pos - 1
a = 0
for v in d.values():
if len(UpperCAmelCase__ ) % 2 != 0:
middle += 1
else:
a = 0
for i in range(0 , len(UpperCAmelCase__ ) ):
if v[i] + v[len(UpperCAmelCase__ ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 32
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Any = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = '''biogpt'''
def __init__( self : List[str] , __lowerCAmelCase : Optional[int]=4_2384 , __lowerCAmelCase : List[Any]=1024 , __lowerCAmelCase : Union[str, Any]=24 , __lowerCAmelCase : int=16 , __lowerCAmelCase : List[Any]=4096 , __lowerCAmelCase : List[str]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : Optional[int]=1024 , __lowerCAmelCase : List[str]=0.0_2 , __lowerCAmelCase : Tuple=1E-12 , __lowerCAmelCase : Any=True , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : Any=0.0 , __lowerCAmelCase : int=0.0 , __lowerCAmelCase : List[Any]=1 , __lowerCAmelCase : Dict=0 , __lowerCAmelCase : Any=2 , **__lowerCAmelCase : int , ) -> Optional[int]:
"""simple docstring"""
a = vocab_size
a = max_position_embeddings
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = initializer_range
a = layer_norm_eps
a = scale_embedding
a = use_cache
a = layerdrop
a = activation_dropout
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
| 32
|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class _lowercase :
def __init__( self : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=13 , __lowerCAmelCase : Any=7 , __lowerCAmelCase : int=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Any=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : str=99 , __lowerCAmelCase : List[str]=64 , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Dict=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : List[str]=0.1 , __lowerCAmelCase : List[str]=512 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Optional[Any]=0.0_2 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : Union[str, Any]=None , ) -> List[str]:
"""simple docstring"""
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = embedding_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
def A ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a = None
a = None
a = None
if self.use_labels:
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = ids_tensor([self.batch_size] , self.num_choices )
a = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : int ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
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 , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , )
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
a = MobileBertModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase , token_type_ids=__lowerCAmelCase )
a = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A ( self : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : Any ) -> str:
"""simple docstring"""
a = MobileBertForMaskedLM(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
a = MobileBertForNextSentencePrediction(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def A ( self : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
a = MobileBertForPreTraining(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , next_sentence_label=__lowerCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] ) -> Any:
"""simple docstring"""
a = MobileBertForQuestionAnswering(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , start_positions=__lowerCAmelCase , end_positions=__lowerCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForSequenceClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
a = self.num_labels
a = MobileBertForTokenClassification(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Union[str, Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
a = self.num_choices
a = MobileBertForMultipleChoice(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a = model(
__lowerCAmelCase , attention_mask=__lowerCAmelCase , token_type_ids=__lowerCAmelCase , labels=__lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) = config_and_inputs
a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
_UpperCAmelCase = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': MobileBertModel,
'''fill-mask''': MobileBertForMaskedLM,
'''question-answering''': MobileBertForQuestionAnswering,
'''text-classification''': MobileBertForSequenceClassification,
'''token-classification''': MobileBertForTokenClassification,
'''zero-shot''': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
def A ( self : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Any=False ) -> Any:
"""simple docstring"""
a = super()._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
if return_labels:
if model_class in get_values(__lowerCAmelCase ):
a = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowerCAmelCase )
a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowerCAmelCase )
return inputs_dict
def A ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
a = MobileBertModelTester(self )
a = ConfigTester(self , config_class=__lowerCAmelCase , hidden_size=37 )
def A ( self : int ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : str ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase )
def A ( self : str ) -> str:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase )
def A ( self : List[str] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase )
def A ( self : int ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase )
def A ( self : List[Any] ) -> int:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Dict:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase )
def A ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase )
def A ( self : int ) -> Tuple:
"""simple docstring"""
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase )
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
A_ : Dict = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowercase ( unittest.TestCase ):
@slow
def A ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__lowerCAmelCase )
a = _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
with torch.no_grad():
a = model(__lowerCAmelCase )[0]
a = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , __lowerCAmelCase )
a = torch.tensor(
[
[
[-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
[-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
[2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
]
] , device=__lowerCAmelCase , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
a = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
a = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 32
| 1
|
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_ : int = logging.get_logger(__name__)
A_ : Union[str, 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_ : Union[str, Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def UpperCAmelCase__ ( UpperCAmelCase__ :str ):
'''simple docstring'''
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
a = model_type_to_module_name(UpperCAmelCase__ )
a = importlib.import_module(F""".{module_name}""" , "transformers.models" )
try:
return getattr(UpperCAmelCase__ , UpperCAmelCase__ )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(UpperCAmelCase__ , "__name__" , UpperCAmelCase__ ) == 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.
a = importlib.import_module("transformers" )
if hasattr(UpperCAmelCase__ , UpperCAmelCase__ ):
return getattr(UpperCAmelCase__ , UpperCAmelCase__ )
return None
def UpperCAmelCase__ ( UpperCAmelCase__ :Union[str, os.PathLike] , UpperCAmelCase__ :Optional[Union[str, os.PathLike]] = None , UpperCAmelCase__ :bool = False , UpperCAmelCase__ :bool = False , UpperCAmelCase__ :Optional[Dict[str, str]] = None , UpperCAmelCase__ :Optional[Union[bool, str]] = None , UpperCAmelCase__ :Optional[str] = None , UpperCAmelCase__ :bool = False , **UpperCAmelCase__ :Any , ):
'''simple docstring'''
a = get_file_from_repo(
UpperCAmelCase__ , UpperCAmelCase__ , cache_dir=UpperCAmelCase__ , force_download=UpperCAmelCase__ , resume_download=UpperCAmelCase__ , proxies=UpperCAmelCase__ , use_auth_token=UpperCAmelCase__ , revision=UpperCAmelCase__ , local_files_only=UpperCAmelCase__ , )
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(UpperCAmelCase__ , encoding="utf-8" ) as reader:
return json.load(UpperCAmelCase__ )
class _lowercase :
def __init__( self : Tuple ) -> Dict:
"""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(__lowerCAmelCase )
def A ( cls : Any , __lowerCAmelCase : str , **__lowerCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
a = kwargs.pop("config" , __lowerCAmelCase )
a = kwargs.pop("trust_remote_code" , __lowerCAmelCase )
a = True
a , a = ImageProcessingMixin.get_image_processor_dict(__lowerCAmelCase , **__lowerCAmelCase )
a = config_dict.get("image_processor_type" , __lowerCAmelCase )
a = None
if "AutoImageProcessor" in config_dict.get("auto_map" , {} ):
a = 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:
a = config_dict.pop("feature_extractor_type" , __lowerCAmelCase )
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." )
a = feature_extractor_class.replace("FeatureExtractor" , "ImageProcessor" )
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
a = config_dict["auto_map"]["AutoFeatureExtractor"]
a = 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(__lowerCAmelCase , __lowerCAmelCase ):
a = AutoConfig.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
# It could be in `config.image_processor_type``
a = getattr(__lowerCAmelCase , "image_processor_type" , __lowerCAmelCase )
if hasattr(__lowerCAmelCase , "auto_map" ) and "AutoImageProcessor" in config.auto_map:
a = config.auto_map["AutoImageProcessor"]
if image_processor_class is not None:
a = image_processor_class_from_name(__lowerCAmelCase )
a = image_processor_auto_map is not None
a = image_processor_class is not None or type(__lowerCAmelCase ) in IMAGE_PROCESSOR_MAPPING
a = resolve_trust_remote_code(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if has_remote_code and trust_remote_code:
a = get_class_from_dynamic_module(
__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
a = kwargs.pop("code_revision" , __lowerCAmelCase )
if os.path.isdir(__lowerCAmelCase ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
elif image_processor_class is not None:
return image_processor_class.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(__lowerCAmelCase ) in IMAGE_PROCESSOR_MAPPING:
a = IMAGE_PROCESSOR_MAPPING[type(__lowerCAmelCase )]
return image_processor_class.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
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 A ( __lowerCAmelCase : Dict , __lowerCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
IMAGE_PROCESSOR_MAPPING.register(__lowerCAmelCase , __lowerCAmelCase )
| 32
|
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _lowercase ( UpperCAmelCase__ ):
def A ( self : Optional[int] , __lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)" )
a = input_file.read()
a = regexp.search(__lowerCAmelCase )
return match
def A ( self : List[Any] , __lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
with open(__lowerCAmelCase , encoding="utf-8" ) as input_file:
a = re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL )
a = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
a = regexp.finditer(__lowerCAmelCase )
a = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def A ( self : List[str] ) -> List[Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__lowerCAmelCase ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def A ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
a = Path("./datasets" )
a = list(dataset_paths.absolute().glob("**/*.py" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__lowerCAmelCase ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 32
| 1
|
from math import sqrt
def UpperCAmelCase__ ( 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(sqrt(UpperCAmelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCAmelCase__ ( UpperCAmelCase__ :int = 1_00_01 ):
'''simple docstring'''
a = 0
a = 1
while count != nth and number < 3:
number += 1
if is_prime(UpperCAmelCase__ ):
count += 1
while count != nth:
number += 2
if is_prime(UpperCAmelCase__ ):
count += 1
return number
if __name__ == "__main__":
print(F"""{solution() = }""")
| 32
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFormerConfig''',
'''InstructBlipVisionConfig''',
],
'''processing_instructblip''': ['''InstructBlipProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : List[Any] = [
'''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InstructBlipQFormerModel''',
'''InstructBlipPreTrainedModel''',
'''InstructBlipForConditionalGeneration''',
'''InstructBlipVisionModel''',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
A_ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 32
| 1
|
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