





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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]



    @register_to_config
    def __init__(       self   ,				A_   ,				A_   ,				A_ = None   ,				A_ = 5_02_57   ,				A_ = 10_24   ,				A_ = 7_68   ,				A_ = 12   ,				A_ = 12   ,				A_ = None   ,				A_ = "gelu_new"   ,				A_ = 0.1   ,				A_ = 0.1   ,				A_ = 0.1   ,				A_ = 1E-5   ,				A_ = 0.02   ,				A_ = True   ,				A_ = True   ,				A_ = False   ,				A_ = False   ,				):





        '''simple docstring'''

        super().__init__()

        SCREAMING_SNAKE_CASE__											=			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.'''     )

        SCREAMING_SNAKE_CASE__											=			prefix_inner_dim
        SCREAMING_SNAKE_CASE__											=			prefix_hidden_dim

        SCREAMING_SNAKE_CASE__											=			(
            nn.Linear(self.prefix_inner_dim   ,				self.prefix_hidden_dim     )
            if self.prefix_hidden_dim is not None
            else nn.Identity()
        )
        SCREAMING_SNAKE_CASE__											=			(
            nn.Linear(self.prefix_hidden_dim   ,				A_     ) if self.prefix_hidden_dim is not None else nn.Identity()
        )

        SCREAMING_SNAKE_CASE__											=			GPTaConfig(
            vocab_size=A_   ,				n_positions=A_   ,				n_embd=A_   ,				n_layer=A_   ,				n_head=A_   ,				n_inner=A_   ,				activation_function=A_   ,				resid_pdrop=A_   ,				embd_pdrop=A_   ,				attn_pdrop=A_   ,				layer_norm_epsilon=A_   ,				initializer_range=A_   ,				scale_attn_weights=A_   ,				use_cache=A_   ,				scale_attn_by_inverse_layer_idx=A_   ,				reorder_and_upcast_attn=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			GPTaLMHeadModel(A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = None   ,				A_ = None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.transformer.transformer.wte(A_     )
        SCREAMING_SNAKE_CASE__											=			self.encode_prefix(A_     )
        SCREAMING_SNAKE_CASE__											=			self.decode_prefix(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.cat((prefix_embeds, embedding_text)   ,				dim=1     )

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



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        return torch.zeros(A_   ,				self.prefix_length   ,				dtype=torch.intaa   ,				device=A_     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        return self.encode_prefix(A_     )



    @torch.no_grad()
    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.split(A_   ,				1   ,				dim=0     )
        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        for feature in features:
            SCREAMING_SNAKE_CASE__											=			self.decode_prefix(feature.to(A_     )     )  # back to the clip feature
            # Only support beam search for now
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.generate_beam(
                input_embeds=A_   ,				device=A_   ,				eos_token_id=A_     )
            generated_tokens.append(output_tokens[0]     )
            generated_seq_lengths.append(seq_lengths[0]     )
        SCREAMING_SNAKE_CASE__											=			torch.stack(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.stack(A_     )
        return generated_tokens, generated_seq_lengths




    @torch.no_grad()
    def  lowercase_					(       self   ,				A_=None   ,				A_=None   ,				A_=None   ,				A_ = 5   ,				A_ = 67   ,				A_ = 1.0   ,				A_ = None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			eos_token_id
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			torch.ones(A_   ,				device=A_   ,				dtype=torch.int     )
        SCREAMING_SNAKE_CASE__											=			torch.zeros(A_   ,				device=A_   ,				dtype=torch.bool     )

        if input_embeds is not None:
            SCREAMING_SNAKE_CASE__											=			input_embeds
        else:
            SCREAMING_SNAKE_CASE__											=			self.transformer.transformer.wte(A_     )

        for i in range(A_     ):
            SCREAMING_SNAKE_CASE__											=			self.transformer(inputs_embeds=A_     )
            SCREAMING_SNAKE_CASE__											=			outputs.logits
            SCREAMING_SNAKE_CASE__											=			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
            SCREAMING_SNAKE_CASE__											=			logits.softmax(-1     ).log()

            if scores is None:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			logits.topk(A_   ,				-1     )
                SCREAMING_SNAKE_CASE__											=			generated.expand(A_   ,				*generated.shape[1:]     )
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			next_tokens.permute(1   ,				0     ), scores.squeeze(0     )
                if tokens is None:
                    SCREAMING_SNAKE_CASE__											=			next_tokens
                else:
                    SCREAMING_SNAKE_CASE__											=			tokens.expand(A_   ,				*tokens.shape[1:]     )
                    SCREAMING_SNAKE_CASE__											=			torch.cat((tokens, next_tokens)   ,				dim=1     )
            else:
                SCREAMING_SNAKE_CASE__											=			-float(np.inf     )
                SCREAMING_SNAKE_CASE__											=			0
                SCREAMING_SNAKE_CASE__											=			scores[:, None] + logits
                seq_lengths[~is_stopped] += 1
                SCREAMING_SNAKE_CASE__											=			scores_sum / seq_lengths[:, None]
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			scores_sum_average.view(-1     ).topk(A_   ,				-1     )
                SCREAMING_SNAKE_CASE__											=			next_tokens // scores_sum.shape[1]
                SCREAMING_SNAKE_CASE__											=			seq_lengths[next_tokens_source]
                SCREAMING_SNAKE_CASE__											=			next_tokens % scores_sum.shape[1]
                SCREAMING_SNAKE_CASE__											=			next_tokens.unsqueeze(1     )
                SCREAMING_SNAKE_CASE__											=			tokens[next_tokens_source]
                SCREAMING_SNAKE_CASE__											=			torch.cat((tokens, next_tokens)   ,				dim=1     )
                SCREAMING_SNAKE_CASE__											=			generated[next_tokens_source]
                SCREAMING_SNAKE_CASE__											=			scores_sum_average * seq_lengths
                SCREAMING_SNAKE_CASE__											=			is_stopped[next_tokens_source]

            SCREAMING_SNAKE_CASE__											=			self.transformer.transformer.wte(next_tokens.squeeze()     ).view(generated.shape[0]   ,				1   ,				-1     )
            SCREAMING_SNAKE_CASE__											=			torch.cat((generated, next_token_embed)   ,				dim=1     )
            SCREAMING_SNAKE_CASE__											=			is_stopped + next_tokens.eq(A_     ).squeeze()
            if is_stopped.all():
                break

        SCREAMING_SNAKE_CASE__											=			scores / seq_lengths
        SCREAMING_SNAKE_CASE__											=			scores.argsort(descending=A_     )
        # tokens tensors are already padded to max_seq_length
        SCREAMING_SNAKE_CASE__											=			[tokens[i] for i in order]
        SCREAMING_SNAKE_CASE__											=			torch.stack(A_   ,				dim=0     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([seq_lengths[i] for i in order]   ,				dtype=seq_lengths.dtype     )
        return output_texts, seq_lengths



'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = UNetaDModel(
          block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,				)
      return model


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=2   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""   ,				return_dict=__SCREAMING_SNAKE_CASE     )[0]

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE      = image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2







@slow
@require_torch
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """google/ncsnpp-celebahq-256"""
      __SCREAMING_SNAKE_CASE      = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = KarrasVeScheduler()

      __SCREAMING_SNAKE_CASE      = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE     )
      pipe.to(__SCREAMING_SNAKE_CASE     )
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=20   ,				generator=__SCREAMING_SNAKE_CASE   ,				output_type="""numpy"""     ).images

      __SCREAMING_SNAKE_CASE      = image[0, -3:, -3:, -1]
      assert image.shape == (1, 256, 256, 3)
      __SCREAMING_SNAKE_CASE      = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






