





'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
      from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
    """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
    """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
    """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
    """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
    """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''bloom'''
    UpperCamelCase     = ['''past_key_values''']
    UpperCamelCase     = {
        '''num_hidden_layers''': '''n_layer''',
        '''num_attention_heads''': '''n_head''',
    }


    def __init__(		self ,		A=25_0880 ,		A=64 ,		A=2 ,		A=8 ,		A=1e-5 ,		A=0.02 ,		A=True ,		A=1 ,		A=2 ,		A=False ,		A=0.0 ,		A=0.0 ,		A=1 ,		A=False ,		**A ,		)		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			vocab_size
        # Backward compatibility with n_embed kwarg
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""n_embed""" ,		A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size if n_embed is None else n_embed
        _SCREAMING_SNAKE_CASE									=			n_layer
        _SCREAMING_SNAKE_CASE									=			n_head
        _SCREAMING_SNAKE_CASE									=			layer_norm_epsilon
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			pretraining_tp
        _SCREAMING_SNAKE_CASE									=			apply_residual_connection_post_layernorm
        _SCREAMING_SNAKE_CASE									=			hidden_dropout
        _SCREAMING_SNAKE_CASE									=			attention_dropout

        _SCREAMING_SNAKE_CASE									=			bos_token_id
        _SCREAMING_SNAKE_CASE									=			eos_token_id
        _SCREAMING_SNAKE_CASE									=			slow_but_exact

        super().__init__(bos_token_id=A ,		eos_token_id=A ,		**A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = version.parse('''1.12'''       )


    def __init__(		self ,		A ,		A = "default" ,		A = None ,		A = False ,		)		->   str:
        super().__init__(A ,		task=A ,		patching_specs=A ,		use_past=A       )
        if not getattr(self._config ,		"""pad_token_id""" ,		A       ):
            # TODO: how to do that better?
            _SCREAMING_SNAKE_CASE									=			0


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        _SCREAMING_SNAKE_CASE									=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
        if self.use_past:
            # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
            self.fill_with_past_key_values_(A ,		direction="""inputs""" ,		inverted_values_shape=A       )
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """past_sequence + sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}

        return common_inputs


    @property
    def 			snake_case_(		self       )		->   int:
        return self._config.n_layer


    @property
    def 			snake_case_(		self       )		->   int:
        return self._config.n_head


    @property
    def 			snake_case_(		self       )		->   float:
        return 1e-3


    def 			snake_case_(		self ,		A ,		A = -1 ,		A = -1 ,		A = False ,		A = None ,		)		->   Mapping[str, Any]:
        _SCREAMING_SNAKE_CASE									=			super(A ,		self       ).generate_dummy_inputs(
            A ,		batch_size=A ,		seq_length=A ,		is_pair=A ,		framework=A       )

        # We need to order the input in the way they appears in the forward()
        _SCREAMING_SNAKE_CASE									=			OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
            else:
                import torch

                _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			common_inputs["""input_ids"""].shape
                # Not using the same length for past_key_values
                _SCREAMING_SNAKE_CASE									=			seqlen + 2
                _SCREAMING_SNAKE_CASE									=			self._config.hidden_size // self.num_attention_heads
                _SCREAMING_SNAKE_CASE									=			(
                    batch * self.num_attention_heads,
                    head_dim,
                    past_key_values_length,
                )
                _SCREAMING_SNAKE_CASE									=			(
                    batch * self.num_attention_heads,
                    past_key_values_length,
                    head_dim,
                )
                _SCREAMING_SNAKE_CASE									=			[
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

        _SCREAMING_SNAKE_CASE									=			common_inputs["""attention_mask"""]
        if self.use_past:
            _SCREAMING_SNAKE_CASE									=			ordered_inputs["""attention_mask"""].dtype
            _SCREAMING_SNAKE_CASE									=			torch.cat(
                [ordered_inputs["""attention_mask"""], torch.ones(A ,		A ,		dtype=A       )] ,		dim=1       )

        return ordered_inputs





    @property
    def 			snake_case_(		self       )		->   int:
        return 13










'''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



