






'''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


lowerCAmelCase__          =						logging.get_logger(__name__)







class       __lowercase					:

 def __init__(      self   :						Any  ,					UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						int):
   UpperCamelCase__				:					str					=					question_encoder
   UpperCamelCase__				:					Optional[int]					=					generator
   UpperCamelCase__				:					Optional[int]					=					self.question_encoder

 def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Any):
   if os.path.isfile(UpperCAmelCase_):
     raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
   os.makedirs(UpperCAmelCase_  ,					exist_ok=UpperCAmelCase_)
   UpperCamelCase__				:					int					=					os.path.join(UpperCAmelCase_  ,					'question_encoder_tokenizer')
   UpperCamelCase__				:					Any					=					os.path.join(UpperCAmelCase_  ,					'generator_tokenizer')
   self.question_encoder.save_pretrained(UpperCAmelCase_)
   self.generator.save_pretrained(UpperCAmelCase_)

 @classmethod
 def      __UpperCamelCase							(      cls   :						str  ,					UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						List[Any]):
   # dynamically import AutoTokenizer
   from ..auto.tokenization_auto import AutoTokenizer

   UpperCamelCase__				:					Tuple					=					kwargs.pop('config'  ,					UpperCAmelCase_)

   if config is None:
     UpperCamelCase__				:					int					=					RagConfig.from_pretrained(UpperCAmelCase_)

   UpperCamelCase__				:					int					=					AutoTokenizer.from_pretrained(
       UpperCAmelCase_  ,					config=config.question_encoder  ,					subfolder='question_encoder_tokenizer')
   UpperCamelCase__				:					Dict					=					AutoTokenizer.from_pretrained(
       UpperCAmelCase_  ,					config=config.generator  ,					subfolder='generator_tokenizer')
   return cls(question_encoder=UpperCAmelCase_  ,					generator=UpperCAmelCase_)

 def __call__(      self   :						str  ,					*UpperCAmelCase_   :						Optional[Any]  ,					**UpperCAmelCase_   :						Any):
   return self.current_tokenizer(*UpperCAmelCase_  ,					**UpperCAmelCase_)

 def      __UpperCamelCase							(      self   :						str  ,					*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						str):
   return self.generator.batch_decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

 def      __UpperCamelCase							(      self   :						Tuple  ,					*UpperCAmelCase_   :						Optional[Any]  ,					**UpperCAmelCase_   :						Dict):
   return self.generator.decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

 def      __UpperCamelCase							(      self   :						Any):
   UpperCamelCase__				:					List[Any]					=					self.question_encoder

 def      __UpperCamelCase							(      self   :						str):
   UpperCamelCase__				:					str					=					self.generator





 def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						List[str]  ,					UpperCAmelCase_   :						Optional[List[str]] = None  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						str = "longest"  ,					UpperCAmelCase_   :						str = None  ,					UpperCAmelCase_   :						bool = True  ,					**UpperCAmelCase_   :						Tuple  ,					):
   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'  ,					UpperCAmelCase_  ,					)
   if max_length is None:
     UpperCamelCase__				:					int					=					self.current_tokenizer.model_max_length
   UpperCamelCase__				:					List[Any]					=					self(
       UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					max_length=UpperCAmelCase_  ,					padding=UpperCAmelCase_  ,					truncation=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)
   if tgt_texts is None:
     return model_inputs
   # Process tgt_texts
   if max_target_length is None:
     UpperCamelCase__				:					List[Any]					=					self.current_tokenizer.model_max_length
   UpperCamelCase__				:					Tuple					=					self(
       text_target=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					padding=UpperCAmelCase_  ,					max_length=UpperCAmelCase_  ,					truncation=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)
   UpperCamelCase__				:					List[str]					=					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       __lowercase					:

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Dict					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					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)
			UpperCamelCase__				:					Optional[Any]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					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)
			UpperCamelCase__				:					List[Any]					=					IFWatermarker()

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

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

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

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					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=1.4_14  ,					time_embedding_act_fn='gelu'  ,					time_embedding_dim=32  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					str					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					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)
			UpperCamelCase__				:					List[str]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					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   :						Any):
			UpperCamelCase__				:					Dict					=					self.get_dummy_components()
			UpperCamelCase__				:					List[Any]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					inputs['prompt']
			UpperCamelCase__				:					List[Any]					=					inputs['generator']
			UpperCamelCase__				:					Tuple					=					inputs['num_inference_steps']
			UpperCamelCase__				:					List[Any]					=					inputs['output_type']

			if "image" in inputs:
					UpperCamelCase__				:					Tuple					=					inputs['image']
			else:
					UpperCamelCase__				:					Union[str, Any]					=					None

			if "mask_image" in inputs:
					UpperCamelCase__				:					Optional[int]					=					inputs['mask_image']
			else:
					UpperCamelCase__				:					int					=					None

			if "original_image" in inputs:
					UpperCamelCase__				:					List[Any]					=					inputs['original_image']
			else:
					UpperCamelCase__				:					Optional[Any]					=					None

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					pipe.encode_prompt(UpperCAmelCase_)

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					List[Any]					=					{
			    '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:
					UpperCamelCase__				:					Dict					=					image

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

			if original_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					original_image

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

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					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.'  ,					)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					inputs['generator']
			UpperCamelCase__				:					List[Any]					=					inputs['num_inference_steps']
			UpperCamelCase__				:					Optional[int]					=					inputs['output_type']

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					Any					=					{
			    '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:
					UpperCamelCase__				:					Tuple					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					str					=					original_image

			UpperCamelCase__				:					Union[str, Any]					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Dict					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Any					=					self.get_dummy_components()
			UpperCamelCase__				:					List[str]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					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

			UpperCamelCase__				:					Any					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Optional[int]					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)




