




'''simple docstring'''


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    __lowercase						(  __magic_name__       ,							__magic_name__       ,							__magic_name__ ):
   _a							=      [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]







   @register_to_config
   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = 5_0257      ,							UpperCamelCase = 1024      ,							UpperCamelCase = 768      ,							UpperCamelCase = 12      ,							UpperCamelCase = 12      ,							UpperCamelCase = None      ,							UpperCamelCase = "gelu_new"      ,							UpperCamelCase = 0.1      ,							UpperCamelCase = 0.1      ,							UpperCamelCase = 0.1      ,							UpperCamelCase = 1e-5      ,							UpperCamelCase = 0.02      ,							UpperCamelCase = True      ,							UpperCamelCase = True      ,							UpperCamelCase = False      ,							UpperCamelCase = False      ,							)				->    int:
     super().__init__()

     __a			=					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."      )

     __a			=					prefix_inner_dim
     __a			=					prefix_hidden_dim

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

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







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

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







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     return self.encode_prefix(UpperCamelCase      )







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







   @torch.no_grad()
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase = 5      ,							UpperCamelCase = 67      ,							UpperCamelCase = 1.0      ,							UpperCamelCase = None      ,							)				->    Tuple:
     __a			=					eos_token_id
     __a			=					None
     __a			=					None
     __a			=					torch.ones(UpperCamelCase      ,							device=UpperCamelCase      ,							dtype=torch.int      )
     __a			=					torch.zeros(UpperCamelCase      ,							device=UpperCamelCase      ,							dtype=torch.bool      )

     if input_embeds is not None:
       __a			=					input_embeds
     else:
       __a			=					self.transformer.transformer.wte(UpperCamelCase      )

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

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

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

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









'''simple docstring'''


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class    __lowercase						:







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=1      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=3      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=[1, 2]      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=6      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							class_embed_type='timestep'      ,							mid_block_scale_factor=1.414      ,							time_embedding_act_fn='gelu'      ,							time_embedding_dim=32      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['prompt']
     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     if "image" in inputs:
       __a			=					inputs['image']
     else:
       __a			=					None

     if "mask_image" in inputs:
       __a			=					inputs['mask_image']
     else:
       __a			=					None

     if "original_image" in inputs:
       __a			=					inputs['original_image']
     else:
       __a			=					None

     __a	,       __a			=					pipe.encode_prompt(UpperCamelCase      )

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
       setattr(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(UpperCamelCase      ,							UpperCamelCase      ) is None      ,							f"`{optional_component}` did not stay set to None after loading."      ,							)

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )



