
"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class A_      (lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						KandinskyPriorPipeline
  SCREAMING_SNAKE_CASE__     :		Any    	=						["""prompt"""]
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						["""prompt""", """negative_prompt"""]
  SCREAMING_SNAKE_CASE__     :		int    	=						[
      """num_images_per_prompt""",
      """generator""",
      """num_inference_steps""",
      """latents""",
      """negative_prompt""",
      """guidance_scale""",
      """output_type""",
      """return_dict""",
  ]
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						False







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 32







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 32







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.time_input_dim







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self.time_input_dim * 4







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 100







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
     return tokenizer







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )
     UpperCAmelCase_   :   Any     	=       CLIPTextConfig(
         bos_token_id=0							,  eos_token_id=2							,  hidden_size=self.text_embedder_hidden_size							,  projection_dim=self.text_embedder_hidden_size							,  intermediate_size=37							,  layer_norm_eps=1E-0_5							,  num_attention_heads=4							,  num_hidden_layers=5							,  pad_token_id=1							,  vocab_size=1000							,  )
     return CLIPTextModelWithProjection(lowercase_    )







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )

     UpperCAmelCase_   :   Dict     	=       {
         "num_attention_heads": 2,
         "attention_head_dim": 12,
         "embedding_dim": self.text_embedder_hidden_size,
         "num_layers": 1,
     }

     UpperCAmelCase_   :   Dict     	=       PriorTransformer(**lowercase_    )
     # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
     UpperCAmelCase_   :   List[Any]     	=       nn.Parameter(torch.ones(model.clip_std.shape    )    )
     return model







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     torch.manual_seed(0    )
     UpperCAmelCase_   :   Tuple     	=       CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size							,  image_size=224							,  projection_dim=self.text_embedder_hidden_size							,  intermediate_size=37							,  num_attention_heads=4							,  num_channels=3							,  num_hidden_layers=5							,  patch_size=14							,  )

     UpperCAmelCase_   :   List[Any]     	=       CLIPVisionModelWithProjection(lowercase_    )
     return model







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       CLIPImageProcessor(
         crop_size=224							,  do_center_crop=lowercase_							,  do_normalize=lowercase_							,  do_resize=lowercase_							,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,  resample=3							,  size=224							,  )

     return image_processor







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.dummy_prior
     UpperCAmelCase_   :   int     	=       self.dummy_image_encoder
     UpperCAmelCase_   :   Union[str, Any]     	=       self.dummy_text_encoder
     UpperCAmelCase_   :   List[Any]     	=       self.dummy_tokenizer
     UpperCAmelCase_   :   List[Any]     	=       self.dummy_image_processor

     UpperCAmelCase_   :   Dict     	=       UnCLIPScheduler(
         variance_type="fixed_small_log"							,  prediction_type="sample"							,  num_train_timesteps=1000							,  clip_sample=lowercase_							,  clip_sample_range=10.0							,  )

     UpperCAmelCase_   :   Optional[int]     	=       {
         "prior": prior,
         "image_encoder": image_encoder,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "scheduler": scheduler,
         "image_processor": image_processor,
     }

     return components







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_=0    ):

     """simple docstring"""
     if str(lowercase_    ).startswith("mps"    ):
        UpperCAmelCase_   :   List[str]     	=       torch.manual_seed(lowercase_    )
     else:
        UpperCAmelCase_   :   Dict     	=       torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
     UpperCAmelCase_   :   Any     	=       {
         "prompt": "horse",
         "generator": generator,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       "cpu"

     UpperCAmelCase_   :   str     	=       self.get_dummy_components()

     UpperCAmelCase_   :   Optional[int]     	=       self.pipeline_class(**lowercase_    )
     UpperCAmelCase_   :   Union[str, Any]     	=       pipe.to(lowercase_    )

     pipe.set_progress_bar_config(disable=lowercase_    )

     UpperCAmelCase_   :   Optional[int]     	=       pipe(**self.get_dummy_inputs(lowercase_    )    )
     UpperCAmelCase_   :   int     	=       output.image_embeds

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

     UpperCAmelCase_   :   Optional[Any]     	=       image[0, -10:]
     UpperCAmelCase_   :   List[str]     	=       image_from_tuple[0, -10:]

     assert image.shape == (1, 32)

     UpperCAmelCase_   :   Optional[int]     	=       np.array(
         [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]    )

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







  @skip_mps
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       torch_device == "cpu"
     UpperCAmelCase_   :   Union[str, Any]     	=       True
     UpperCAmelCase_   :   Optional[Any]     	=       False

     self._test_inference_batch_single_identical(
         test_max_difference=lowercase_							,  relax_max_difference=lowercase_							,  test_mean_pixel_difference=lowercase_							,  )







  @skip_mps
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       torch_device == "cpu"
     UpperCAmelCase_   :   str     	=       False

     self._test_attention_slicing_forward_pass(
         test_max_difference=lowercase_							,  test_mean_pixel_difference=lowercase_							,  )



"""simple docstring"""




import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


__lowerCamelCase									=	"block_records.npy"


__lowerCamelCase									=	logging.get_logger(__name__)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							import tensorflow.compat.va as tf

							A__						  =				tf.data.TFRecordDataset(UpperCamelCase__							,     buffer_size=512 * 1_024 * 1_024					)
							A__						  =				blocks_dataset.batch(UpperCamelCase__							,     drop_remainder=UpperCamelCase__					)
							A__						  =				next(blocks_dataset.take(1					).as_numpy_iterator()					)

							return np_record
class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=2		,__UpperCAmelCase=10_00		,__UpperCAmelCase=1_00		,__UpperCAmelCase=10_00_00		,)       ->					Optional[int]:
													from scann.scann_ops.py.scann_ops_pybind import builder as Builder

													A__						  =				Builder(db=__UpperCAmelCase		,num_neighbors=__UpperCAmelCase		,distance_measure='dot_product'  )
													A__						  =				builder.tree(
													    num_leaves=__UpperCAmelCase		,num_leaves_to_search=__UpperCAmelCase		,training_sample_size=__UpperCAmelCase  )
													A__						  =				builder.score_ah(dimensions_per_block=__UpperCAmelCase  )

													A__						  =				builder.build()





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													A__  ,	A__						  =				self.searcher.search_batched(question_projection.detach().cpu()  )
													return retrieved_block_ids.astype('int64'  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[str]:
													super().__init__()
													A__						  =				block_records
													A__						  =				tokenizer





						def __call__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None		,__UpperCAmelCase="pt"  )       ->					str:
													A__						  =				np.take(self.block_records		,indices=__UpperCAmelCase		,axis=0  )

													A__						  =				self.tokenizer.decode(question_input_ids[0]		,skip_special_tokens=__UpperCAmelCase  )

													A__						  =				[]
													A__						  =				[]
													for retrieved_block in retrieved_blocks:
																				text.append(__UpperCAmelCase  )
																				text_pair.append(retrieved_block.decode()  )

													A__						  =				self.tokenizer(
													    __UpperCAmelCase		,__UpperCAmelCase		,padding=__UpperCAmelCase		,truncation=__UpperCAmelCase		,return_special_tokens_mask=__UpperCAmelCase		,max_length=__UpperCAmelCase  )
													A__						  =				concat_inputs.convert_to_tensors(__UpperCAmelCase  )

													if answer_ids is not None:
																				return self.block_has_answer(__UpperCAmelCase		,__UpperCAmelCase  ) + (concat_inputs_tensors,)
													else:
																				return (None, None, None, concat_inputs_tensors)





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					List[Any]:
													if os.path.isdir(__UpperCAmelCase  ):
																				A__						  =				os.path.join(__UpperCAmelCase		,_REALM_BLOCK_RECORDS_FILENAME  )
													else:
																				A__						  =				hf_hub_download(
																				    repo_id=__UpperCAmelCase		,filename=_REALM_BLOCK_RECORDS_FILENAME		,**__UpperCAmelCase  )
													A__						  =				np.load(__UpperCAmelCase		,allow_pickle=__UpperCAmelCase  )

													A__						  =				AutoTokenizer.from_pretrained(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )

													return cls(__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													# save block records
													np.save(os.path.join(__UpperCAmelCase		,_REALM_BLOCK_RECORDS_FILENAME  )		,self.block_records  )
													# save tokenizer
													self.tokenizer.save_pretrained(__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[Any]:
													A__						  =				[]
													A__						  =				[]
													A__						  =				[]
													A__						  =				0

													for input_id in concat_inputs.input_ids:
																				A__						  =				input_id.tolist()
																				# Check answers between two [SEP] tokens
																				A__						  =				input_id_list.index(self.tokenizer.sep_token_id  )
																				A__						  =				first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id  )

																				start_pos.append([]  )
																				end_pos.append([]  )
																				for answer in answer_ids:
																											for idx in range(first_sep_idx + 1		,__UpperCAmelCase  ):
																																		if answer[0] == input_id_list[idx]:
																																									if input_id_list[idx : idx + len(__UpperCAmelCase  )] == answer:
																																																start_pos[-1].append(__UpperCAmelCase  )
																																																end_pos[-1].append(idx + len(__UpperCAmelCase  ) - 1  )

																				if len(start_pos[-1]  ) == 0:
																											has_answers.append(__UpperCAmelCase  )
																				else:
																											has_answers.append(__UpperCAmelCase  )
																											if len(start_pos[-1]  ) > max_answers:
																																		A__						  =				len(start_pos[-1]  )

        # Pad -1 to max_answers
													for start_pos_, end_pos_ in zip(__UpperCAmelCase		,__UpperCAmelCase  ):
																				if len(start_pos_  ) < max_answers:
																											A__						  =				[-1] * (max_answers - len(start_pos_  ))
																											start_pos_ += padded
																											end_pos_ += padded
													return has_answers, start_pos, end_pos





