




"""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


__A 		=			"block_records.npy"


__A 		=			logging.get_logger(__name__)



def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     int   ):
		import tensorflow.compat.va as tf

		snake_case		:						Union[str, Any]								=				tf.data.TFRecordDataset(lowercase__       ,     buffer_size=512 * 1024 * 1024   )
		snake_case		:						Any								=				blocks_dataset.batch(lowercase__       ,     drop_remainder=lowercase__   )
		snake_case		:						Optional[int]								=				next(blocks_dataset.take(1   ).as_numpy_iterator()   )

		return np_record
class 			lowerCamelCase__    :


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=1_000				, SCREAMING_SNAKE_CASE=100				, SCREAMING_SNAKE_CASE=100_000				, ):



					"""simple docstring"""







					from scann.scann_ops.py.scann_ops_pybind import builder as Builder

					snake_case		:						Any								=				Builder(db=SCREAMING_SNAKE_CASE				, num_neighbors=SCREAMING_SNAKE_CASE				, distance_measure="dot_product"							)
					snake_case		:						Any								=				builder.tree(
					    num_leaves=SCREAMING_SNAKE_CASE				, num_leaves_to_search=SCREAMING_SNAKE_CASE				, training_sample_size=SCREAMING_SNAKE_CASE							)
					snake_case		:						Dict								=				builder.score_ah(dimensions_per_block=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				builder.build()



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case			,				snake_case		:						int								=				self.searcher.search_batched(question_projection.detach().cpu()							)
					return retrieved_block_ids.astype("int64"							)







class 			lowerCamelCase__    :


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()
					snake_case		:						Any								=				block_records
					snake_case		:						str								=				tokenizer


			def __call__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="pt"							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				np.take(self.block_records				, indices=SCREAMING_SNAKE_CASE				, axis=0							)

					snake_case		:						Dict								=				self.tokenizer.decode(question_input_ids[0]				, skip_special_tokens=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				[]
					snake_case		:						Optional[Any]								=				[]
					for retrieved_block in retrieved_blocks:
							text.append(SCREAMING_SNAKE_CASE							)
							text_pair.append(retrieved_block.decode()							)

					snake_case		:						Union[str, Any]								=				self.tokenizer(
					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, truncation=SCREAMING_SNAKE_CASE				, return_special_tokens_mask=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				concat_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE							)

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


			@classmethod
			def 							lowerCamelCase_				(     cls				, SCREAMING_SNAKE_CASE				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if os.path.isdir(SCREAMING_SNAKE_CASE							):
							snake_case		:						Optional[Any]								=				os.path.join(SCREAMING_SNAKE_CASE				, _REALM_BLOCK_RECORDS_FILENAME							)
					else:
							snake_case		:						str								=				hf_hub_download(
							    repo_id=SCREAMING_SNAKE_CASE				, filename=_REALM_BLOCK_RECORDS_FILENAME				, **SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				np.load(SCREAMING_SNAKE_CASE				, allow_pickle=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					return cls(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					np.save(os.path.join(SCREAMING_SNAKE_CASE				, _REALM_BLOCK_RECORDS_FILENAME							)				, self.block_records							)
					# save tokenizer
					self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Any								=				[]
					snake_case		:						str								=				[]
					snake_case		:						Any								=				[]
					snake_case		:						List[Any]								=				0

					for input_id in concat_inputs.input_ids:
							snake_case		:						Union[str, Any]								=				input_id.tolist()
							# Check answers between two [SEP] tokens
							snake_case		:						Union[str, Any]								=				input_id_list.index(self.tokenizer.sep_token_id							)
							snake_case		:						Optional[int]								=				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				, SCREAMING_SNAKE_CASE							):
											if answer[0] == input_id_list[idx]:
													if input_id_list[idx : idx + len(SCREAMING_SNAKE_CASE							)] == answer:
															start_pos[-1].append(SCREAMING_SNAKE_CASE							)
															end_pos[-1].append(idx + len(SCREAMING_SNAKE_CASE							) - 1							)

							if len(start_pos[-1]							) == 0:
									has_answers.append(SCREAMING_SNAKE_CASE							)
							else:
									has_answers.append(SCREAMING_SNAKE_CASE							)
									if len(start_pos[-1]							) > max_answers:
											snake_case		:						List[str]								=				len(start_pos[-1]							)

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







"""simple docstring"""

import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      int								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Union[str, Any]								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 3]				, SCREAMING_SNAKE_CASE=[1, 3]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     """config.json"""


			@register_to_config
			def __init__(     self				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=5				, SCREAMING_SNAKE_CASE=(2, 5)				, SCREAMING_SNAKE_CASE="for diffusion"				, SCREAMING_SNAKE_CASE=[1, 5]				, SCREAMING_SNAKE_CASE=[5, 4]				, ):



					"""simple docstring"""







					pass







class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							ConfigMixin.load_config("dummy_path"							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				SampleObject()
					snake_case		:						Dict								=				obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# init ignore private arguments
					snake_case		:						Dict								=				SampleObject(_name_or_path="lalala"							)
					snake_case		:						List[Any]								=				obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can override default
					snake_case		:						str								=				SampleObject(c=6							)
					snake_case		:						Any								=				obj.config
					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					# can use positional arguments.
					snake_case		:						int								=				SampleObject(1				, c=6							)
					snake_case		:						Dict								=				obj.config
					assert config["a"] == 1
					assert config["b"] == 5
					assert config["c"] == 6
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				SampleObject()
					snake_case		:						List[str]								=				obj.config

					assert config["a"] == 2
					assert config["b"] == 5
					assert config["c"] == (2, 5)
					assert config["d"] == "for diffusion"
					assert config["e"] == [1, 3]

					with tempfile.TemporaryDirectory() as tmpdirname:
							obj.save_config(SCREAMING_SNAKE_CASE							)
							snake_case		:						Union[str, Any]								=				SampleObject.from_config(SampleObject.load_config(SCREAMING_SNAKE_CASE							)							)
							snake_case		:						Union[str, Any]								=				new_obj.config

					# unfreeze configs
					snake_case		:						List[str]								=				dict(SCREAMING_SNAKE_CASE							)
					snake_case		:						Union[str, Any]								=				dict(SCREAMING_SNAKE_CASE							)

					assert config.pop("c"							) == (2, 5)  # instantiated as tuple
					assert new_config.pop("c"							) == [2, 5]  # saved & loaded as list because of json
					config.pop("_use_default_values"							)
					assert config == new_config


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Any								=				DDIMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert ddim.__class__ == DDIMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Optional[int]								=				EulerDiscreteScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert euler.__class__ == EulerDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Any								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				EulerAncestralDiscreteScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert euler.__class__ == EulerAncestralDiscreteScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				PNDMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert pndm.__class__ == PNDMScheduler
					# no warning should be thrown
					assert cap_logger.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						List[Any]								=				DDPMScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"				, prediction_type="sample"				, beta_end=8				, )

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger_a:
							snake_case		:						Optional[int]								=				DDPMScheduler.from_pretrained("google/ddpm-celebahq-256"				, beta_start=88							)

					assert ddpm.__class__ == DDPMScheduler
					assert ddpm.config.prediction_type == "sample"
					assert ddpm.config.beta_end == 8
					assert ddpm_a.config.beta_start == 88

					# no warning should be thrown
					assert cap_logger.out == ""
					assert cap_logger_a.out == ""


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				logging.get_logger("diffusers.configuration_utils"							)
					# 30 for warning
					logger.setLevel(30							)

					with CaptureLogger(SCREAMING_SNAKE_CASE							) as cap_logger:
							snake_case		:						Union[str, Any]								=				DPMSolverMultistepScheduler.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-torch"				, subfolder="scheduler"							)

					assert dpm.__class__ == DPMSolverMultistepScheduler
					# no warning should be thrown
					assert cap_logger.out == ""



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				SampleObject()

					snake_case		:						str								=				{k: v for k, v in config.config.items() if not k.startswith("_"							)}

					# make sure that default config has all keys in `_use_default_values`
					assert set(config_dict.keys()							) == set(config.config._use_default_values							)

					with tempfile.TemporaryDirectory() as tmpdirname:
							config.save_config(SCREAMING_SNAKE_CASE							)

							# now loading it with SampleObject2 should put f into `_use_default_values`
							snake_case		:						Optional[int]								=				SampleObjecta.from_config(SCREAMING_SNAKE_CASE							)

							assert "f" in config._use_default_values
							assert config.f == [1, 3]

					# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
					# **BECAUSE** it is part of `config._use_default_values`
					snake_case		:						Tuple								=				SampleObjecta.from_config(config.config							)
					assert new_config.f == [5, 4]

					config.config._use_default_values.pop()
					snake_case		:						List[Any]								=				SampleObjecta.from_config(config.config							)
					assert new_config_a.f == [1, 3]

					# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
					assert new_config_a.e == [1, 3]

