'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
 from PIL import Image







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Dict      	=					(
     """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
     """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
     """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
 )
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					"""CIDAS/clipseg-rd64-refined"""
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""image_segmenter"""
 _SCREAMING_SNAKE_CASE     :       int      	=					CLIPSegForImageSegmentation

 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["""image""", """text"""]
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					["""image"""]
 def __init__(      self   :	str      ,       *SCREAMING_SNAKE_CASE__   :	str      ,       **SCREAMING_SNAKE_CASE__   :	Tuple							)  ->    Any:
  requires_backends(self      ,       ["""vision"""]							)
  super().__init__(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	"Image"      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    Union[str, Any]:
  return self.pre_processor(text=[label]      ,       images=[image]      ,       padding=SCREAMING_SNAKE_CASE__      ,       return_tensors="""pt"""							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    Union[str, Any]:
  with torch.no_grad():
   __lowerCAmelCase											=						self.model(**SCREAMING_SNAKE_CASE__							).logits
  return logits





 def a					(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    List[Any]:
  __lowerCAmelCase											=						outputs.cpu().detach().numpy()
  __lowerCAmelCase											=						0
  __lowerCAmelCase											=						1
  return Image.fromarray((array * 2_55).astype(np.uinta							)							)







'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=64      ,       SCREAMING_SNAKE_CASE__   :	int=None							)  ->    Optional[int]:
  __lowerCAmelCase											=						np.random.default_rng(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						length
  __lowerCAmelCase											=						rng.normal(size=(length,)							).astype(np.floataa							)
  __lowerCAmelCase											=						a * self.x + b + rng.normal(scale=0.1      ,       size=(length,)							).astype(np.floataa							)
 def __len__(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  return self.length





 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[int]:
  return {"x": self.x[i], "y": self.y[i]}







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None							)  ->    str:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a[0] + self.b[0]







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Optional[Any]:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    int:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a + self.b







def      UpperCamelCase_						(					snake_case_       :       List[str]						,		snake_case_       :       int = 16      )					->     int:






 '''simple docstring'''

 from datasets import load_dataset
 from transformers import AutoTokenizer

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 __lowerCAmelCase											=						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 __lowerCAmelCase											=						load_dataset("""csv"""						,		data_files=snake_case_      )
 __lowerCAmelCase											=						datasets["""train"""].unique("""label"""      )

 __lowerCAmelCase											=						{v: i for i, v in enumerate(snake_case_      )}

 def tokenize_function(snake_case_       :       Optional[int]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(
      examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=snake_case_						,		max_length=snake_case_						,		padding="""max_length"""      )
  if "label" in examples:
   __lowerCAmelCase											=						[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""sentence1""", """sentence2""", """label"""]						,		)

 def collate_fn(snake_case_       :       List[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=2      )
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=1      )

 return train_dataloader, eval_dataloader






