
'''simple docstring'''

import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


snake_case									=    logging.get_logger(__name__)




class    lowerCAmelCase       :


 def __init__(    self  : Tuple			,							a__  : Union[str, Any]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	question_encoder
  lowerCAmelCase__     :   Dict     		=	generator
  lowerCAmelCase__     :   int     		=	self.question_encoder


 def    _A	(    self  : int			,							a__  : List[str]    ):
  '''simple docstring'''

  if os.path.isfile(a__    ):
   raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''    )
  os.makedirs(a__			,							exist_ok=a__    )
  lowerCAmelCase__     :   Dict     		=	os.path.join(a__			,							"question_encoder_tokenizer"    )
  lowerCAmelCase__     :   List[str]     		=	os.path.join(a__			,							"generator_tokenizer"    )
  self.question_encoder.save_pretrained(a__    )
  self.generator.save_pretrained(a__    )


 @classmethod
 def    _A	(    cls  : Union[str, Any]			,							a__  : List[Any]			,							**a__  : Tuple    ):
  '''simple docstring'''

  from ..auto.tokenization_auto import AutoTokenizer

  lowerCAmelCase__     :   Any     		=	kwargs.pop("config"			,							a__    )

  if config is None:
   lowerCAmelCase__     :   Any     		=	RagConfig.from_pretrained(a__    )

  lowerCAmelCase__     :   List[Any]     		=	AutoTokenizer.from_pretrained(
      a__			,							config=config.question_encoder			,							subfolder="question_encoder_tokenizer"    )
  lowerCAmelCase__     :   Optional[Any]     		=	AutoTokenizer.from_pretrained(
      a__			,							config=config.generator			,							subfolder="generator_tokenizer"    )
  return cls(question_encoder=a__			,							generator=a__    )


 def __call__(    self  : Dict			,							*a__  : List[Any]			,							**a__  : Optional[int]    ):
  '''simple docstring'''

  return self.current_tokenizer(*a__			,							**a__    )


 def    _A	(    self  : int			,							*a__  : str			,							**a__  : int    ):
  '''simple docstring'''

  return self.generator.batch_decode(*a__			,							**a__    )


 def    _A	(    self  : str			,							*a__  : Any			,							**a__  : int    ):
  '''simple docstring'''

  return self.generator.decode(*a__			,							**a__    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.question_encoder


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.generator


 def    _A	(    self  : List[Any]			,							a__  : List[str]			,							a__  : Optional[List[str]] = None			,							a__  : Optional[int] = None			,							a__  : Optional[int] = None			,							a__  : str = "longest"			,							a__  : str = None			,							a__  : bool = True			,							**a__  : str			,							):
  '''simple docstring'''

  warnings.warn(
      "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
      "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
      "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
      "details"			,							a__			,							)
  if max_length is None:
   lowerCAmelCase__     :   str     		=	self.current_tokenizer.model_max_length
  lowerCAmelCase__     :   Optional[int]     		=	self(
      a__			,							add_special_tokens=a__			,							return_tensors=a__			,							max_length=a__			,							padding=a__			,							truncation=a__			,							**a__			,							)
  if tgt_texts is None:
   return model_inputs
  # Process tgt_texts
  if max_target_length is None:
   lowerCAmelCase__     :   List[Any]     		=	self.current_tokenizer.model_max_length
  lowerCAmelCase__     :   str     		=	self(
      text_target=a__			,							add_special_tokens=a__			,							return_tensors=a__			,							padding=a__			,							max_length=a__			,							truncation=a__			,							**a__			,							)
  lowerCAmelCase__     :   Optional[Any]     		=	labels["input_ids"]
  return model_inputs







'''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    lowerCAmelCase       :


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[str]     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Optional[int]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Any     		=	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    )
  lowerCAmelCase__     :   Union[str, Any]     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	IFWatermarker()

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


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Any     		=	TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   List[Any]     		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							thresholding=a__			,							dynamic_thresholding_ratio=0.95			,							sample_max_value=1.0			,							prediction_type="epsilon"			,							variance_type="learned_range"			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Tuple     		=	DDPMScheduler(
      num_train_timesteps=1000			,							beta_schedule="squaredcos_cap_v2"			,							beta_start=0.0001			,							beta_end=0.02			,							)

  torch.manual_seed(0    )
  lowerCAmelCase__     :   Dict     		=	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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Dict     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Tuple     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   str     		=	inputs["prompt"]
  lowerCAmelCase__     :   Optional[Any]     		=	inputs["generator"]
  lowerCAmelCase__     :   str     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   int     		=	inputs["output_type"]

  if "image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "mask_image" in inputs:
   lowerCAmelCase__     :   List[str]     		=	inputs["mask_image"]
  else:
   lowerCAmelCase__     :   str     		=	None

  if "original_image" in inputs:
   lowerCAmelCase__     :   Optional[int]     		=	inputs["original_image"]
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	None

  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	pipe.encode_prompt(a__    )

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   Dict     		=	{
      "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:
   lowerCAmelCase__     :   Optional[Any]     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   Optional[int]     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   Optional[Any]     		=	original_image

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

  lowerCAmelCase__     :   Optional[int]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

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

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

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )

  lowerCAmelCase__     :   Any     		=	inputs["generator"]
  lowerCAmelCase__     :   Union[str, Any]     		=	inputs["num_inference_steps"]
  lowerCAmelCase__     :   str     		=	inputs["output_type"]

  # inputs with prompt converted to embeddings
  lowerCAmelCase__     :   int     		=	{
      "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:
   lowerCAmelCase__     :   Tuple     		=	image

  if mask_image is not None:
   lowerCAmelCase__     :   int     		=	mask_image

  if original_image is not None:
   lowerCAmelCase__     :   List[Any]     		=	original_image

  lowerCAmelCase__     :   Dict     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   int     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.get_dummy_components()
  lowerCAmelCase__     :   int     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   str     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

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

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(to_np(a__    ) - to_np(a__    )    ).max()
  self.assertLess(a__			,							1e-4    )





