



"""simple docstring"""


import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			[
    """yjernite/retribert-base-uncased""",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				RetriBertConfig
						__UpperCamelCase													=				None
						__UpperCamelCase													=				"""retribert"""






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:int   )							->     Union[str, Any]:
												if isinstance(lowercase_  ,    nn.Linear   ):
																		module.weight.data.normal_(mean=0.0  ,    std=self.config.initializer_range   )
																		if module.bias is not None:
																								module.bias.data.zero_()
												elif isinstance(lowercase_  ,    nn.Embedding   ):
																		module.weight.data.normal_(mean=0.0  ,    std=self.config.initializer_range   )
																		if module.padding_idx is not None:
																								module.weight.data[module.padding_idx].zero_()
												elif isinstance(lowercase_  ,    nn.LayerNorm   ):
																		module.bias.data.zero_()
																		module.weight.data.fill_(1.0   )


snake_case_								=			R"""

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""







@add_start_docstrings(
    """Bert Based model to embed queries or document for document retrieval."""      ,  SCREAMING_SNAKE_CASE_      ,  )
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Tuple  ,    lowercase_					:RetriBertConfig   )							->     None:
												super().__init__(lowercase_   )
												UpperCAmelCase			 =					config.projection_dim

												UpperCAmelCase			 =					BertModel(lowercase_   )
												UpperCAmelCase			 =					None if config.share_encoders else BertModel(lowercase_   )
												UpperCAmelCase			 =					nn.Dropout(config.hidden_dropout_prob   )
												UpperCAmelCase			 =					nn.Linear(config.hidden_size  ,    config.projection_dim  ,    bias=lowercase_   )
												UpperCAmelCase			 =					nn.Linear(config.hidden_size  ,    config.projection_dim  ,    bias=lowercase_   )

												UpperCAmelCase			 =					nn.CrossEntropyLoss(reduction='mean'   )

												# Initialize weights and apply final processing
												self.post_init()






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:Any  ,    lowercase_					:Dict  ,    lowercase_					:List[Any]  ,    lowercase_					:Dict=-1  ,    )							->     Optional[Any]:
												# reproduces BERT forward pass with checkpointing
												if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
																		return sent_encoder(lowercase_  ,    attention_mask=lowercase_   )[1]
												else:
																		# prepare implicit variables
																		UpperCAmelCase			 =					input_ids.device
																		UpperCAmelCase			 =					input_ids.size()
																		UpperCAmelCase			 =					torch.zeros(lowercase_  ,    dtype=torch.long  ,    device=lowercase_   )
																		UpperCAmelCase			 =					[None] * sent_encoder.config.num_hidden_layers
																		UpperCAmelCase			 =					sent_encoder.get_extended_attention_mask(
																		    lowercase_  ,    lowercase_   )

																		# define function for checkpointing
																		def partial_encode(*lowercase_					:Optional[Any]   ):
																								UpperCAmelCase			 =					sent_encoder.encoder(
																								    inputs[0]  ,    attention_mask=inputs[1]  ,    head_mask=lowercase_  ,    )
																								UpperCAmelCase			 =					encoder_outputs[0]
																								UpperCAmelCase			 =					sent_encoder.pooler(lowercase_   )
																								return pooled_output

																		# run embedding layer on everything at once
																		UpperCAmelCase			 =					sent_encoder.embeddings(
																		    input_ids=lowercase_  ,    position_ids=lowercase_  ,    token_type_ids=lowercase_  ,    inputs_embeds=lowercase_   )
																		# run encoding and pooling on one mini-batch at a time
																		UpperCAmelCase			 =					[]
																		for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
																								UpperCAmelCase			 =					embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																								UpperCAmelCase			 =					extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																								UpperCAmelCase			 =					checkpoint.checkpoint(lowercase_  ,    lowercase_  ,    lowercase_   )
																								pooled_output_list.append(lowercase_   )
																		return torch.cat(lowercase_  ,    dim=0   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Any  ,    lowercase_					:Tuple=None  ,    lowercase_					:Union[str, Any]=-1  ,    )							->     int:
												UpperCAmelCase			 =					self.embed_sentences_checkpointed(
												    lowercase_  ,    lowercase_  ,    self.bert_query  ,    lowercase_  ,    )
												return self.project_query(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Any  ,    lowercase_					:int=None  ,    lowercase_					:Any=-1  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.embed_sentences_checkpointed(
												    lowercase_  ,    lowercase_  ,    self.bert_query if self.bert_doc is None else self.bert_doc  ,    lowercase_  ,    )
												return self.project_doc(lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.LongTensor  ,    lowercase_					:Optional[torch.FloatTensor]  ,    lowercase_					:torch.LongTensor  ,    lowercase_					:Optional[torch.FloatTensor]  ,    lowercase_					:int = -1  ,    )							->     torch.FloatTensor:
												UpperCAmelCase			 =					input_ids_query.device
												UpperCAmelCase			 =					self.embed_questions(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					self.embed_answers(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					torch.mm(lowercase_  ,    a_reps.t()   )
												UpperCAmelCase			 =					self.ce_loss(lowercase_  ,    torch.arange(compare_scores.shape[1]   ).to(lowercase_   )   )
												UpperCAmelCase			 =					self.ce_loss(compare_scores.t()  ,    torch.arange(compare_scores.shape[0]   ).to(lowercase_   )   )
												UpperCAmelCase			 =					(loss_qa + loss_aq) / 2
												return loss







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class     a_       (			a__				,    a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  StableDiffusionModelEditingPipeline
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  TEXT_TO_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					int						=  TEXT_TO_IMAGE_BATCH_PARAMS
       __SCREAMING_SNAKE_CASE       :					Any						=  TEXT_TO_IMAGE_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  TEXT_TO_IMAGE_IMAGE_PARAMS






       def 	__lowerCAmelCase     (		self   )					->int:
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Dict				      = UNetaDConditionModel(
               block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=4				,       out_channels=4				,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,       cross_attention_dim=32				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = DDIMScheduler()
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = AutoencoderKL(
               block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,       up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,       latent_channels=4				,       )
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = CLIPTextConfig(
               bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1000				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = CLIPTextModel(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=0   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               # Setting height and width to None to prevent OOMs on CPU.
               '''height''': None,
               '''width''': None,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Any				      = image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				List[str]				      = np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]   )

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






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''french fries'''
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe(**_lowerCamelCase				,       negative_prompt=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = output.images
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]   )

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






       def 	__lowerCAmelCase     (		self   )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				List[Any]				      = EulerAncestralDiscreteScheduler(
               beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       beta_schedule='''scaled_linear'''   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE				:				str				      = np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]   )

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






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = PNDMScheduler()
           SCREAMING_SNAKE_CASE				:				Tuple				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           # the pipeline does not expect pndm so test if it raises error.
           with self.assertRaises(_lowerCamelCase   ):
               SCREAMING_SNAKE_CASE				:				Dict				      = sd_pipe(**_lowerCamelCase   ).images






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().test_inference_batch_single_identical(expected_max_diff=5e-3   )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )






@slow
@require_torch_gpu
class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=0   )					->Dict:
           SCREAMING_SNAKE_CASE				:				str				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				List[str]				      = StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing()

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           SCREAMING_SNAKE_CASE				:				str				      = np.array(
               [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]   )

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

           # make sure image changes after editing
           pipe.edit_model('''A pack of roses'''				,       '''A pack of blue roses'''   )

           SCREAMING_SNAKE_CASE				:				Any				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				List[str]				      = image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(expected_slice - image_slice   ).max() > 1e-1






       def 	__lowerCAmelCase     (		self   )					->str:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				Any				      = DDIMScheduler.from_pretrained(_lowerCamelCase				,       subfolder='''scheduler'''   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline.from_pretrained(
               _lowerCamelCase				,       scheduler=_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing(1   )
           pipe.enable_sequential_cpu_offload()

           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.cuda.max_memory_allocated()
           # make sure that less than 4.4 GB is allocated
           assert mem_bytes < 4.4 * 10**9
