





'''simple docstring'''

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

      lowercase_					= """0.12"""  # assumed parallelism: 8


if is_torch_available():
      import torch





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    Optional[int]=None    )						->Tuple:
    if rng is None:
        _SCREAMING_SNAKE_CASE									=			random.Random()

    _SCREAMING_SNAKE_CASE									=			1
    for dim in shape:
        total_dims *= dim

    _SCREAMING_SNAKE_CASE									=			[]
    for _ in range(__lowerCamelCase    ):
        values.append(rng.randint(0					,       vocab_size - 1    )    )

    _SCREAMING_SNAKE_CASE									=			np.array(__lowerCamelCase					,       dtype=jnp.intaa    ).reshape(__lowerCamelCase    )

    return output





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[Any]=None    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			ids_tensor(__lowerCamelCase					,       vocab_size=2					,       rng=__lowerCamelCase    )
    # make sure that at least one token is attended to for each batch
    _SCREAMING_SNAKE_CASE									=			1
    return attn_mask


@require_flax
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = None
    UpperCamelCase     = ()


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			inputs["""input_ids"""].shape[-1] // 2
        _SCREAMING_SNAKE_CASE									=			inputs["""input_ids"""][:max_batch_size, :sequence_length]

        _SCREAMING_SNAKE_CASE									=			jnp.ones_like(A       )
        _SCREAMING_SNAKE_CASE									=			attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        _SCREAMING_SNAKE_CASE									=			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()`
            _SCREAMING_SNAKE_CASE									=			config.eos_token_id
        return config, input_ids, attention_mask, max_length


    @is_pt_flax_cross_test
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			False
        _SCREAMING_SNAKE_CASE									=			max_length
        _SCREAMING_SNAKE_CASE									=			0

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
            _SCREAMING_SNAKE_CASE									=			getattr(A ,		A       )
            _SCREAMING_SNAKE_CASE									=			pt_model_class(A       ).eval()
            _SCREAMING_SNAKE_CASE									=			load_flax_weights_in_pytorch_model(A ,		flax_model.params       )

            _SCREAMING_SNAKE_CASE									=			flax_model.generate(A       ).sequences
            _SCREAMING_SNAKE_CASE									=			pt_model.generate(torch.tensor(A ,		dtype=torch.long       )       )

            if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                _SCREAMING_SNAKE_CASE									=			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

            self.assertListEqual(pt_generation_outputs.numpy().tolist() ,		flax_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			False
        _SCREAMING_SNAKE_CASE									=			max_length

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			max_length

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			False
        _SCREAMING_SNAKE_CASE									=			max_length
        _SCREAMING_SNAKE_CASE									=			2

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			False
        _SCREAMING_SNAKE_CASE									=			max_length
        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			2

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[0] ,		input_ids.shape[0] * config.num_return_sequences       )


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			max_length
        _SCREAMING_SNAKE_CASE									=			0.8
        _SCREAMING_SNAKE_CASE									=			10
        _SCREAMING_SNAKE_CASE									=			0.3
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			8
        _SCREAMING_SNAKE_CASE									=			9

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			max_length
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			8
        _SCREAMING_SNAKE_CASE									=			9

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()
        _SCREAMING_SNAKE_CASE									=			max_length
        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			1
        _SCREAMING_SNAKE_CASE									=			8
        _SCREAMING_SNAKE_CASE									=			9

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()

        # pad attention mask on the left
        _SCREAMING_SNAKE_CASE									=			attention_mask.at[(0, 0)].set(0       )

        _SCREAMING_SNAKE_CASE									=			False
        _SCREAMING_SNAKE_CASE									=			max_length

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A ,		attention_mask=A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A ,		attention_mask=A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()

        # pad attention mask on the left
        _SCREAMING_SNAKE_CASE									=			attention_mask.at[(0, 0)].set(0       )

        _SCREAMING_SNAKE_CASE									=			True
        _SCREAMING_SNAKE_CASE									=			max_length

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A ,		attention_mask=A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A ,		attention_mask=A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )





    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_input_ids_and_config()

        # pad attention mask on the left
        _SCREAMING_SNAKE_CASE									=			attention_mask.at[(0, 0)].set(0       )

        _SCREAMING_SNAKE_CASE									=			2
        _SCREAMING_SNAKE_CASE									=			max_length

        for model_class in self.all_generative_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

            _SCREAMING_SNAKE_CASE									=			model.generate(A ,		attention_mask=A       ).sequences
            self.assertEqual(generation_outputs.shape[-1] ,		A       )

            _SCREAMING_SNAKE_CASE									=			jit(model.generate       )
            _SCREAMING_SNAKE_CASE									=			jit_generate(A ,		attention_mask=A       ).sequences

            self.assertListEqual(generation_outputs.tolist() ,		jit_generation_outputs.tolist()       )




@require_flax
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""       )
        _SCREAMING_SNAKE_CASE									=			FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""       )

        _SCREAMING_SNAKE_CASE									=			"""Hello world"""
        _SCREAMING_SNAKE_CASE									=			tokenizer(A ,		return_tensors="""np"""       ).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(A ,		"""do_samples"""       ):
            model.generate(A ,		do_samples=A       )

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(A ,		"""foo"""       ):
            _SCREAMING_SNAKE_CASE									=			{"""foo""": """bar"""}
            model.generate(A ,		**A       )










'''simple docstring'''

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

lowercase_					= """Usage of script: script_name <size_of_canvas:int>"""

lowercase_					= [0] * 100 + [1] * 10
random.shuffle(choice)





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			[[False for i in range(__lowerCamelCase    )] for j in range(__lowerCamelCase    )]
    return canvas





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->None:
    for i, row in enumerate(__lowerCamelCase    ):
        for j, _ in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			bool(random.getrandbits(1    )    )





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			np.array(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowerCamelCase    ):
        for c, pt in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			__judge_point(
                __lowerCamelCase					,       current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

    _SCREAMING_SNAKE_CASE									=			next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    _SCREAMING_SNAKE_CASE									=			current_canvas.tolist()
    return return_canvas





def lowerCamelCase						(     __lowerCamelCase :    bool					,       __lowerCamelCase :    list[list[bool]]    )						->bool:
    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    _SCREAMING_SNAKE_CASE									=			pt
    if pt:
        if alive < 2:
            _SCREAMING_SNAKE_CASE									=			False
        elif alive == 2 or alive == 3:
            _SCREAMING_SNAKE_CASE									=			True
        elif alive > 3:
            _SCREAMING_SNAKE_CASE									=			False
    else:
        if alive == 3:
            _SCREAMING_SNAKE_CASE									=			True

    return state


if __name__ == "__main__":
      if len(sys.argv) != 2:
            raise Exception(usage_doc)

      lowercase_					= int(sys.argv[1])
      # main working structure of this module.
      lowercase_					= create_canvas(canvas_size)
      seed(c)
      lowercase_				,			lowercase_					= plt.subplots()
      fig.show()
      lowercase_					= ListedColormap(["""w""", """k"""])
      try:
            while True:
                  lowercase_					= run(c)
                  ax.matshow(c, cmap=cmap)
                  fig.canvas.draw()
                  ax.cla()
      except KeyboardInterrupt:
            # do nothing.
            pass



