'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class lowercase_       :







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			int     )  ->			List[str]:
        return None







class lowercase_       :







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			str     )  ->			int:
        return None







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   [
       # (model_name, model_kwargs)
       ("bert-base-cased", {}),
       ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
   ]
   @require_tf
   @slow
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Union[str, Any]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(UpperCamelCase__, 'tf', 12, **UpperCamelCase__     )
   @require_torch
   @slow
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(UpperCamelCase__, 'pt', 12, **UpperCamelCase__     )
   @require_torch
   @slow
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        from transformers import BertModel

        _A    		=  ['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
        with NamedTemporaryFile(mode='w+t'     ) as vocab_file:
             vocab_file.write('\n'.join(UpperCamelCase__     )     )
             vocab_file.flush()
             _A    		=  BertTokenizerFast(vocab_file.name     )

        with TemporaryDirectory() as bert_save_dir:
             _A    		=  BertModel(BertConfig(vocab_size=len(UpperCamelCase__     )     )     )
             model.save_pretrained(UpperCamelCase__     )
             self._test_export(UpperCamelCase__, 'pt', 12, UpperCamelCase__     )
   @require_tf
   @slow
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             _A    		=  self._test_export(UpperCamelCase__, 'tf', 12, **UpperCamelCase__     )
             _A    		=  quantize(Path(UpperCamelCase__     )     )

             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(UpperCamelCase__     ).stat().st_size:
                  self.fail('Quantized model is bigger than initial ONNX model'     )
   @require_torch
   @slow
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             _A    		=  self._test_export(UpperCamelCase__, 'pt', 12, **UpperCamelCase__     )
             _A    		=  quantize(UpperCamelCase__     )

             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(UpperCamelCase__     ).stat().st_size:
                  self.fail('Quantized model is bigger than initial ONNX model'     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			Dict, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Optional[int]=None, **UpperCamelCase__      :			Optional[Any]     )  ->			Tuple:
        try:
             # Compute path
             with TemporaryDirectory() as tempdir:
                  _A    		=  Path(UpperCamelCase__     ).joinpath('model.onnx'     )

             # Remove folder if exists
             if path.parent.exists():
                  path.parent.rmdir()

             # Export
             convert(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, **UpperCamelCase__     )

             return path
        except Exception as e:
             self.fail(UpperCamelCase__     )
   @require_torch
   @require_tokenizers
   @slow
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        from transformers import BertModel

        _A    		=  BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'     )     )
        _A    		=  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'     )
        self._test_infer_dynamic_axis(UpperCamelCase__, UpperCamelCase__, 'pt'     )
   @require_tf
   @require_tokenizers
   @slow
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        from transformers import TFBertModel

        _A    		=  TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'     )     )
        _A    		=  BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'     )
        self._test_infer_dynamic_axis(UpperCamelCase__, UpperCamelCase__, 'tf'     )
   def 	__UpperCAmelCase   (      self      :			Dict, UpperCamelCase__      :			List[str], UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Tuple     )  ->			Optional[Any]:
        _A    		=  FeatureExtractionPipeline(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  ['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
        _A		,       _A		,       _A		,       _A    		=  infer_shapes(UpperCamelCase__, UpperCamelCase__     )

        # Assert all variables are present
        self.assertEqual(len(UpperCamelCase__     ), len(UpperCamelCase__     )     )
        self.assertTrue(all(var_name in shapes for var_name in variable_names     )     )
        self.assertSequenceEqual(variable_names[:3], UpperCamelCase__     )
        self.assertSequenceEqual(variable_names[3:], UpperCamelCase__     )

        # Assert inputs are {0: batch, 1: sequence}
        for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
             self.assertDictEqual(shapes[var_name], {0: 'batch', 1: 'sequence'}     )

        # Assert outputs are {0: batch, 1: sequence} and {0: batch}
        self.assertDictEqual(shapes['output_0'], {0: 'batch', 1: 'sequence'}     )
        self.assertDictEqual(shapes['output_1'], {0: 'batch'}     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Any:
        _A    		=  ['input_ids', 'attention_mask', 'token_type_ids']
        _A    		=  {'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
        _A		,       _A    		=  ensure_valid_input(FuncContiguousArgs(), UpperCamelCase__, UpperCamelCase__     )

        # Should have exactly the same number of args (all are valid)
        self.assertEqual(len(UpperCamelCase__     ), 3     )

        # Should have exactly the same input names
        self.assertEqual(set(UpperCamelCase__     ), set(UpperCamelCase__     )     )

        # Parameter should be reordered according to their respective place in the function:
        # (input_ids, token_type_ids, attention_mask)
        self.assertEqual(UpperCamelCase__, (tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])     )

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        _A		,       _A    		=  ensure_valid_input(FuncNonContiguousArgs(), UpperCamelCase__, UpperCamelCase__     )

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(UpperCamelCase__     ), 1     )
        self.assertEqual(len(UpperCamelCase__     ), 1     )

        # Should have only "input_ids"
        self.assertEqual(inputs_args[0], tokens['input_ids']     )
        self.assertEqual(ordered_input_names[0], 'input_ids'     )

   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        _A    		=  generate_identified_filename(Path('/home/something/my_fake_model.onnx'     ), '-test'     )
        self.assertEqual('/home/something/my_fake_model-test.onnx', generated.as_posix()     )





'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define glider example
lowercase__						       =      [
    [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
lowercase__						       =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							list[list[int]]:


		'''simple docstring'''


		snake_case  :       Optional[Any]     			=						[]
		for i in range(len(SCREAMING_SNAKE_CASE__			)			):
				snake_case  :       str     			=						[]
				for j in range(len(cells[i]			)			):
						# Get the number of live neighbours
						snake_case  :       Any     			=						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(SCREAMING_SNAKE_CASE__			) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(SCREAMING_SNAKE_CASE__			) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(SCREAMING_SNAKE_CASE__			) - 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  :       List[Any]     			=						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(SCREAMING_SNAKE_CASE__			)
		return next_generation




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							list[Image.Image]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						[]
		for _ in range(SCREAMING_SNAKE_CASE__			):
				# Create output image
				snake_case  :       List[Any]     			=						Image.new('''RGB''' ,       (len(cells[0]			), len(SCREAMING_SNAKE_CASE__			))			)
				snake_case  :       Optional[Any]     			=						img.load()

				# Save cells to image
				for x in range(len(SCREAMING_SNAKE_CASE__			)			):
						for y in range(len(cells[0]			)			):
								snake_case  :       str     			=						255 - cells[y][x] * 255
								snake_case  :       List[str]     			=						(colour, colour, colour)

        # Save image
				images.append(SCREAMING_SNAKE_CASE__			)
				snake_case  :       Dict     			=						new_generation(SCREAMING_SNAKE_CASE__			)
		return images


if __name__ == "__main__":
						lowercase__						       =      generate_images(GLIDER, 1_6)
						images[0].save("out.gif", save_all=True, append_images=images[1:])


