





'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      lowercase_					= """platform"""

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Optional[int]=None					,       __lowerCamelCase :    Optional[int]=None					,       __lowerCamelCase :    int=None					,       __lowerCamelCase :    Tuple=None					,       __lowerCamelCase :    Tuple=None					,       __lowerCamelCase :    Dict=None					,       )						->Union[str, Any]:
    if attention_mask is None:
        _SCREAMING_SNAKE_CASE									=			np.where(input_ids != config.pad_token_id					,       1					,       0    )
    if decoder_attention_mask is None:
        _SCREAMING_SNAKE_CASE									=			np.where(decoder_input_ids != config.pad_token_id					,       1					,       0    )
    if head_mask is None:
        _SCREAMING_SNAKE_CASE									=			np.ones((config.encoder_layers, config.encoder_attention_heads)    )
    if decoder_head_mask is None:
        _SCREAMING_SNAKE_CASE									=			np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    if cross_attn_head_mask is None:
        _SCREAMING_SNAKE_CASE									=			np.ones((config.decoder_layers, config.decoder_attention_heads)    )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }


class 					a_     :

    '''simple docstring'''


    def __init__(		self ,		A ,		A=13 ,		A=7 ,		A=True ,		A=False ,		A=99 ,		A=16 ,		A=2 ,		A=4 ,		A=4 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=32 ,		A=2 ,		A=1 ,		A=0 ,		A=0.02 ,		)		->   int:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			seq_length
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			eos_token_id
        _SCREAMING_SNAKE_CASE									=			pad_token_id
        _SCREAMING_SNAKE_CASE									=			bos_token_id
        _SCREAMING_SNAKE_CASE									=			initializer_range


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,		self.vocab_size       ) ,		3 ,		self.vocab_size       )
        _SCREAMING_SNAKE_CASE									=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,		dtype=np.intaa       )) ,		-1       )

        _SCREAMING_SNAKE_CASE									=			shift_tokens_right(A ,		1 ,		2       )

        _SCREAMING_SNAKE_CASE									=			BlenderbotSmallConfig(
            vocab_size=self.vocab_size ,		d_model=self.hidden_size ,		encoder_layers=self.num_hidden_layers ,		decoder_layers=self.num_hidden_layers ,		encoder_attention_heads=self.num_attention_heads ,		decoder_attention_heads=self.num_attention_heads ,		encoder_ffn_dim=self.intermediate_size ,		decoder_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 ,		initializer_range=self.initializer_range ,		use_cache=A ,		)
        _SCREAMING_SNAKE_CASE									=			prepare_blenderbot_inputs_dict(A ,		A ,		A       )
        return config, inputs_dict


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        return config, inputs_dict


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			20
        _SCREAMING_SNAKE_CASE									=			model_class_name(A       )

        _SCREAMING_SNAKE_CASE									=			model.encode(inputs_dict["""input_ids"""]       )

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _SCREAMING_SNAKE_CASE									=			model.init_cache(decoder_input_ids.shape[0] ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,		dtype="""i4"""       )

        _SCREAMING_SNAKE_CASE									=			jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :] ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,		)
        _SCREAMING_SNAKE_CASE									=			model.decode(
            decoder_input_ids[:, :-1] ,		A ,		decoder_attention_mask=A ,		past_key_values=A ,		decoder_position_ids=A ,		)

        _SCREAMING_SNAKE_CASE									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,		dtype="""i4"""       )
        _SCREAMING_SNAKE_CASE									=			model.decode(
            decoder_input_ids[:, -1:] ,		A ,		decoder_attention_mask=A ,		past_key_values=outputs_cache.past_key_values ,		decoder_position_ids=A ,		)

        _SCREAMING_SNAKE_CASE									=			model.decode(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
        self.parent.assertTrue(diff < 1e-3 ,		msg=f'Max diff is {diff}'       )





    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			20
        _SCREAMING_SNAKE_CASE									=			model_class_name(A       )

        _SCREAMING_SNAKE_CASE									=			model.encode(inputs_dict["""input_ids"""]       )

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _SCREAMING_SNAKE_CASE									=			jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
            ] ,		axis=-1 ,		)

        _SCREAMING_SNAKE_CASE									=			model.init_cache(decoder_input_ids.shape[0] ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :] ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,		)

        _SCREAMING_SNAKE_CASE									=			model.decode(
            decoder_input_ids[:, :-1] ,		A ,		decoder_attention_mask=A ,		past_key_values=A ,		decoder_position_ids=A ,		)
        _SCREAMING_SNAKE_CASE									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,		dtype="""i4"""       )
        _SCREAMING_SNAKE_CASE									=			model.decode(
            decoder_input_ids[:, -1:] ,		A ,		past_key_values=outputs_cache.past_key_values ,		decoder_attention_mask=A ,		decoder_position_ids=A ,		)

        _SCREAMING_SNAKE_CASE									=			model.decode(A ,		A ,		decoder_attention_mask=A       )

        _SCREAMING_SNAKE_CASE									=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
        self.parent.assertTrue(diff < 1e-3 ,		msg=f'Max diff is {diff}'       )




@require_flax
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = 99


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ] ,		dtype=np.intaa ,		)

        _SCREAMING_SNAKE_CASE									=			input_ids.shape[0]
        _SCREAMING_SNAKE_CASE									=			BlenderbotSmallConfig(
            vocab_size=self.vocab_size ,		d_model=24 ,		encoder_layers=2 ,		decoder_layers=2 ,		encoder_attention_heads=2 ,		decoder_attention_heads=2 ,		encoder_ffn_dim=32 ,		decoder_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


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self._get_config_and_data()
        _SCREAMING_SNAKE_CASE									=			FlaxBlenderbotSmallForConditionalGeneration(A       )
        _SCREAMING_SNAKE_CASE									=			lm_model(input_ids=A       )
        _SCREAMING_SNAKE_CASE									=			(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape ,		A       )


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			BlenderbotSmallConfig(
            vocab_size=self.vocab_size ,		d_model=14 ,		encoder_layers=2 ,		decoder_layers=2 ,		encoder_attention_heads=2 ,		decoder_attention_heads=2 ,		encoder_ffn_dim=8 ,		decoder_ffn_dim=8 ,		max_position_embeddings=48 ,		)
        _SCREAMING_SNAKE_CASE									=			FlaxBlenderbotSmallForConditionalGeneration(A       )
        _SCREAMING_SNAKE_CASE									=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,		dtype=np.intaa       )
        _SCREAMING_SNAKE_CASE									=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,		dtype=np.intaa       )
        _SCREAMING_SNAKE_CASE									=			lm_model(input_ids=A ,		decoder_input_ids=A       )
        _SCREAMING_SNAKE_CASE									=			(*summary.shape, config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape ,		A       )





    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,		dtype=np.intaa       )
        _SCREAMING_SNAKE_CASE									=			shift_tokens_right(A ,		1 ,		2       )
        _SCREAMING_SNAKE_CASE									=			np.equal(A ,		1       ).astype(np.floataa       ).sum()
        _SCREAMING_SNAKE_CASE									=			np.equal(A ,		1       ).astype(np.floataa       ).sum()
        self.assertEqual(shifted.shape ,		input_ids.shape       )
        self.assertEqual(A ,		n_pad_before - 1       )
        self.assertTrue(np.equal(shifted[:, 0] ,		2       ).all()       )




@require_flax
class 					a_     (				snake_case_      ,    unittest.TestCase      ,    snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = True
    UpperCamelCase     = (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    UpperCamelCase     = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			FlaxBlenderbotSmallModelTester(self       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(A ,		A ,		A       )


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(A ,		A ,		A       )


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

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__       ):
                _SCREAMING_SNAKE_CASE									=			self._prepare_for_class(A ,		A       )
                _SCREAMING_SNAKE_CASE									=			model_class(A       )

                @jax.jit
                def encode_jitted(A ,		A=None ,		**A       ):
                    return model.encode(input_ids=A ,		attention_mask=A       )

                with self.subTest("""JIT Enabled"""       ):
                    _SCREAMING_SNAKE_CASE									=			encode_jitted(**A       ).to_tuple()

                with self.subTest("""JIT Disabled"""       ):
                    with jax.disable_jit():
                        _SCREAMING_SNAKE_CASE									=			encode_jitted(**A       ).to_tuple()

                self.assertEqual(len(A       ) ,		len(A       )       )
                for jitted_output, output in zip(A ,		A       ):
                    self.assertEqual(jitted_output.shape ,		output.shape       )


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

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__       ):
                _SCREAMING_SNAKE_CASE									=			model_class(A       )
                _SCREAMING_SNAKE_CASE									=			model.encode(inputs_dict["""input_ids"""] ,		inputs_dict["""attention_mask"""]       )

                _SCREAMING_SNAKE_CASE									=			{
                    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                    """encoder_outputs""": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(A ,		A ,		A       ):
                    return model.decode(
                        decoder_input_ids=A ,		decoder_attention_mask=A ,		encoder_outputs=A ,		)

                with self.subTest("""JIT Enabled"""       ):
                    _SCREAMING_SNAKE_CASE									=			decode_jitted(**A       ).to_tuple()

                with self.subTest("""JIT Disabled"""       ):
                    with jax.disable_jit():
                        _SCREAMING_SNAKE_CASE									=			decode_jitted(**A       ).to_tuple()

                self.assertEqual(len(A       ) ,		len(A       )       )
                for jitted_output, output in zip(A ,		A       ):
                    self.assertEqual(jitted_output.shape ,		output.shape       )





    @slow
    def 			snake_case_(		self       )		->   str:
        for model_class_name in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""       )
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            _SCREAMING_SNAKE_CASE									=			np.ones((1, 1)       ) * model.config.eos_token_id
            _SCREAMING_SNAKE_CASE									=			model(A       )
            self.assertIsNotNone(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



