from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[Any]						=      [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]







    @register_to_config
    def __init__(	self , __lowercase , __lowercase , __lowercase = None , __lowercase = 50_257 , __lowercase = 1_024 , __lowercase = 768 , __lowercase = 12 , __lowercase = 12 , __lowercase = None , __lowercase = "gelu_new" , __lowercase = 0.1 , __lowercase = 0.1 , __lowercase = 0.1 , __lowercase = 1E-5 , __lowercase = 0.02 , __lowercase = True , __lowercase = True , __lowercase = False , __lowercase = False , )       ->	Dict:
        super().__init__()

        __UpperCamelCase     :Optional[int]			  =		prefix_length

        if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
            raise ValueError(
                f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
                f""" `n_embd`: {n_embd} are not equal.""")

        __UpperCamelCase     :int			  =		prefix_inner_dim
        __UpperCamelCase     :List[Any]			  =		prefix_hidden_dim

        __UpperCamelCase     :int			  =		(
            nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim)
            if self.prefix_hidden_dim is not None
            else nn.Identity()
        )
        __UpperCamelCase     :Tuple			  =		(
            nn.Linear(self.prefix_hidden_dim , __lowercase) if self.prefix_hidden_dim is not None else nn.Identity()
        )

        __UpperCamelCase     :Union[str, Any]			  =		GPTaConfig(
            vocab_size=__lowercase , n_positions=__lowercase , n_embd=__lowercase , n_layer=__lowercase , n_head=__lowercase , n_inner=__lowercase , activation_function=__lowercase , resid_pdrop=__lowercase , embd_pdrop=__lowercase , attn_pdrop=__lowercase , layer_norm_epsilon=__lowercase , initializer_range=__lowercase , scale_attn_weights=__lowercase , use_cache=__lowercase , scale_attn_by_inverse_layer_idx=__lowercase , reorder_and_upcast_attn=__lowercase , )
        __UpperCamelCase     :List[str]			  =		GPTaLMHeadModel(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , __lowercase = None , )       ->	Any:
        __UpperCamelCase     :Any			  =		self.transformer.transformer.wte(__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		self.encode_prefix(__lowercase)
        __UpperCamelCase     :List[str]			  =		self.decode_prefix(__lowercase)
        __UpperCamelCase     :str			  =		torch.cat((prefix_embeds, embedding_text) , dim=1)

        if labels is not None:
            __UpperCamelCase     :List[Any]			  =		self.get_dummy_token(input_ids.shape[0] , input_ids.device)
            __UpperCamelCase     :int			  =		torch.cat((dummy_token, input_ids) , dim=1)
        __UpperCamelCase     :int			  =		self.transformer(inputs_embeds=__lowercase , labels=__lowercase , attention_mask=__lowercase)
        if self.prefix_hidden_dim is not None:
            return out, hidden
        else:
            return out







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	torch.Tensor:
        return torch.zeros(__lowercase , self.prefix_length , dtype=torch.intaa , device=__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	str:
        return self.encode_prefix(__lowercase)







    @torch.no_grad()
    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[str]:
        __UpperCamelCase     :Tuple			  =		torch.split(__lowercase , 1 , dim=0)
        __UpperCamelCase     :Tuple			  =		[]
        __UpperCamelCase     :Union[str, Any]			  =		[]
        for feature in features:
            __UpperCamelCase     :Optional[Any]			  =		self.decode_prefix(feature.to(__lowercase))  # back to the clip feature
            # Only support beam search for now
            __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		self.generate_beam(
                input_embeds=__lowercase , device=__lowercase , eos_token_id=__lowercase)
            generated_tokens.append(output_tokens[0])
            generated_seq_lengths.append(seq_lengths[0])
        __UpperCamelCase     :Optional[Any]			  =		torch.stack(__lowercase)
        __UpperCamelCase     :str			  =		torch.stack(__lowercase)
        return generated_tokens, generated_seq_lengths







    @torch.no_grad()
    def 			UpperCamelCase__   (	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase = 5 , __lowercase = 67 , __lowercase = 1.0 , __lowercase = None , )       ->	Optional[int]:
        __UpperCamelCase     :Optional[Any]			  =		eos_token_id
        __UpperCamelCase     :str			  =		None
        __UpperCamelCase     :Optional[int]			  =		None
        __UpperCamelCase     :List[Any]			  =		torch.ones(__lowercase , device=__lowercase , dtype=torch.int)
        __UpperCamelCase     :List[str]			  =		torch.zeros(__lowercase , device=__lowercase , dtype=torch.bool)

        if input_embeds is not None:
            __UpperCamelCase     :Dict			  =		input_embeds
        else:
            __UpperCamelCase     :int			  =		self.transformer.transformer.wte(__lowercase)

        for i in range(__lowercase):
            __UpperCamelCase     :List[str]			  =		self.transformer(inputs_embeds=__lowercase)
            __UpperCamelCase     :List[str]			  =		outputs.logits
            __UpperCamelCase     :Union[str, Any]			  =		logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
            __UpperCamelCase     :List[str]			  =		logits.softmax(-1).log()

            if scores is None:
                __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		logits.topk(__lowercase , -1)
                __UpperCamelCase     :List[str]			  =		generated.expand(__lowercase , *generated.shape[1:])
                __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		next_tokens.permute(1 , 0), scores.squeeze(0)
                if tokens is None:
                    __UpperCamelCase     :str			  =		next_tokens
                else:
                    __UpperCamelCase     :List[Any]			  =		tokens.expand(__lowercase , *tokens.shape[1:])
                    __UpperCamelCase     :List[str]			  =		torch.cat((tokens, next_tokens) , dim=1)
            else:
                __UpperCamelCase     :Dict			  =		-float(np.inf)
                __UpperCamelCase     :Tuple			  =		0
                __UpperCamelCase     :List[str]			  =		scores[:, None] + logits
                seq_lengths[~is_stopped] += 1
                __UpperCamelCase     :Optional[Any]			  =		scores_sum / seq_lengths[:, None]
                __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		scores_sum_average.view(-1).topk(__lowercase , -1)
                __UpperCamelCase     :str			  =		next_tokens // scores_sum.shape[1]
                __UpperCamelCase     :Tuple			  =		seq_lengths[next_tokens_source]
                __UpperCamelCase     :str			  =		next_tokens % scores_sum.shape[1]
                __UpperCamelCase     :int			  =		next_tokens.unsqueeze(1)
                __UpperCamelCase     :Any			  =		tokens[next_tokens_source]
                __UpperCamelCase     :Optional[int]			  =		torch.cat((tokens, next_tokens) , dim=1)
                __UpperCamelCase     :Optional[Any]			  =		generated[next_tokens_source]
                __UpperCamelCase     :str			  =		scores_sum_average * seq_lengths
                __UpperCamelCase     :Union[str, Any]			  =		is_stopped[next_tokens_source]

            __UpperCamelCase     :Optional[int]			  =		self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0] , 1 , -1)
            __UpperCamelCase     :int			  =		torch.cat((generated, next_token_embed) , dim=1)
            __UpperCamelCase     :List[str]			  =		is_stopped + next_tokens.eq(__lowercase).squeeze()
            if is_stopped.all():
                break

        __UpperCamelCase     :Any			  =		scores / seq_lengths
        __UpperCamelCase     :Optional[int]			  =		scores.argsort(descending=__lowercase)
        # tokens tensors are already padded to max_seq_length
        __UpperCamelCase     :int			  =		[tokens[i] for i in order]
        __UpperCamelCase     :str			  =		torch.stack(__lowercase , dim=0)
        __UpperCamelCase     :List[str]			  =		torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype)
        return output_texts, seq_lengths






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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    for i in range(len(SCREAMING_SNAKE_CASE	)	):
        __UpperCamelCase     :Dict			  =		[]
        for j in range(len(cells[i]	)	):
            # Get the number of live neighbours
            __UpperCamelCase     :List[str]			  =		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.
            __UpperCamelCase     :List[str]			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		[]
    for _ in range(SCREAMING_SNAKE_CASE	):
        # Create output image
        __UpperCamelCase     :Dict			  =		Image.new('''RGB'''  ,			(len(cells[0]	), len(SCREAMING_SNAKE_CASE	))	)
        __UpperCamelCase     :Any			  =		img.load()

        # Save cells to image
        for x in range(len(SCREAMING_SNAKE_CASE	)	):
            for y in range(len(cells[0]	)	):
                __UpperCamelCase     :Optional[Any]			  =		255 - cells[y][x] * 255
                __UpperCamelCase     :int			  =		(colour, colour, colour)

        # Save image
        images.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Optional[int]			  =		new_generation(SCREAMING_SNAKE_CASE	)
    return images


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





