




'''simple docstring'''

from __future__ import annotations

from PIL import Image

# Define glider example
a  :				Optional[int]									=  [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [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],
]

# Define blinker example
a  :				List[Any]									=  [[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def 		__magic_name__    (	__UpperCAmelCase	)		->					list[list[int]]:

					'''simple docstring'''

					snake_case_									=				[]
					for i in range(len(__UpperCAmelCase	)	):
										snake_case_									=				[]
										for j in range(len(cells[i]	)	):
															# Get the number of live neighbours
															snake_case_									=				0
															if i > 0 and j > 0:
																				neighbour_count += cells[i - 1][j - 1]
															if i > 0:
																				neighbour_count += cells[i - 1][j]
															if i > 0 and j < len(cells[i]	) - 1:
																				neighbour_count += cells[i - 1][j + 1]
															if j > 0:
																				neighbour_count += cells[i][j - 1]
															if j < len(cells[i]	) - 1:
																				neighbour_count += cells[i][j + 1]
															if i < len(__UpperCAmelCase	) - 1 and j > 0:
																				neighbour_count += cells[i + 1][j - 1]
															if i < len(__UpperCAmelCase	) - 1:
																				neighbour_count += cells[i + 1][j]
															if i < len(__UpperCAmelCase	) - 1 and j < len(cells[i]	) - 1:
																				neighbour_count += cells[i + 1][j + 1]

															# Rules of the game of life (excerpt from Wikipedia):
															# 1. Any live cell with two or three live neighbours survives.
															# 2. Any dead cell with three live neighbours becomes a live cell.
															# 3. All other live cells die in the next generation.
															#    Similarly, all other dead cells stay dead.
															snake_case_									=				cells[i][j] == 1
															if (
															    (alive and 2 <= neighbour_count <= 3)
															    or not alive
															    and neighbour_count == 3
															):
																				next_generation_row.append(1	)
															else:
																				next_generation_row.append(0	)

										next_generation.append(__UpperCAmelCase	)
					return next_generation


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					list[Image.Image]:

					'''simple docstring'''

					snake_case_									=				[]
					for _ in range(__UpperCAmelCase	):
										# Create output image
										snake_case_									=				Image.new('''RGB''',      (len(cells[0]	), len(__UpperCAmelCase	))	)
										snake_case_									=				img.load()

										# Save cells to image
										for x in range(len(__UpperCAmelCase	)	):
															for y in range(len(cells[0]	)	):
																				snake_case_									=				255 - cells[y][x] * 255
																				snake_case_									=				(colour, colour, colour)

        # Save image
										images.append(__UpperCAmelCase	)
										snake_case_									=				new_generation(__UpperCAmelCase	)
					return images


if __name__ == "__main__":
							a  :				Dict									=  generate_images(GLIDER, 16)
							images[0].save('out.gif', save_all=True, append_images=images[1:])









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

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 			__a  (    A__       ):
 _lowerCAmelCase       :       str        = '''facebook/bart-large-mnli'''
 _lowerCAmelCase       :       Tuple        = (
     '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
     '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
     '''It returns the most likely label in the list of provided `labels` for the input text.'''
 )
 _lowerCAmelCase       :       Any        = '''text_classifier'''
 _lowerCAmelCase       :       int        = AutoTokenizer
 _lowerCAmelCase       :       Union[str, Any]        = AutoModelForSequenceClassification

 _lowerCAmelCase       :       Union[str, Any]        = ['''text''', ['''text''']]
 _lowerCAmelCase       :       Dict        = ['''text''']







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      super().setup()
      UpperCamelCase__			:   Dict       				=  self.model.config
      UpperCamelCase__			:   Union[str, Any]       				=  -1
      for idx, label in config.idalabel.items():
           if label.lower().startswith("entail"						):
                UpperCamelCase__			:   List[str]       				=  int(SCREAMING_SNAKE_CASE						)
      if self.entailment_id == -1:
           raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."						)







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  labels
      return self.pre_processor(
          [text] * len(SCREAMING_SNAKE_CASE						)							,   [F'This example is {label}' for label in labels]							,   return_tensors="pt"							,   padding="max_length"							,   )







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  outputs.logits
      UpperCamelCase__			:   Any       				=  torch.argmax(logits[:, 2]						).item()
      return self._labels[label_id]