






import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer


class        __UpperCamelCase    (     _lowerCAmelCase   ):







	def  _a   (  self      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  tempfile.mkdtemp()
		__lowercase             =  5

		# Realm tok
		__lowercase             =  [
		    """[UNK]""",
		    """[CLS]""",
		    """[SEP]""",
		    """[PAD]""",
		    """[MASK]""",
		    """test""",
		    """question""",
		    """this""",
		    """is""",
		    """the""",
		    """first""",
		    """second""",
		    """third""",
		    """fourth""",
		    """fifth""",
		    """record""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		__lowercase             =  os.path.join(self.tmpdirname       ,				"""realm_tokenizer"""			)
		os.makedirs(_lowerCAmelCase       ,				exist_ok=_lowerCAmelCase			)
		__lowercase             =  os.path.join(_lowerCAmelCase       ,				VOCAB_FILES_NAMES["""vocab_file"""]			)
		with open(self.vocab_file       ,				"""w"""       ,				encoding="""utf-8"""			) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)

		__lowercase             =  os.path.join(self.tmpdirname       ,				"""realm_block_records"""			)
		os.makedirs(_lowerCAmelCase       ,				exist_ok=_lowerCAmelCase			)







	def  _a   (  self      :							Dict			)		-> RealmTokenizer:
		"""simple docstring"""







		return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				"""realm_tokenizer"""			)			)







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		shutil.rmtree(self.tmpdirname			)







	def  _a   (  self      :							Tuple			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  RealmConfig(num_block_records=self.num_block_records			)
		return config







	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  Dataset.from_dict(
		    {
		        """id""": ["""0""", """1"""],
		        """question""": ["""foo""", """bar"""],
		        """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
		    }			)
		return dataset







	def  _a   (  self      :							Optional[int]			)		-> str:
		"""simple docstring"""







		__lowercase             =  np.array(
		    [
		        B"""This is the first record""",
		        B"""This is the second record""",
		        B"""This is the third record""",
		        B"""This is the fourth record""",
		        B"""This is the fifth record""",
		        B"""This is a longer longer longer record""",
		    ]       ,				dtype=_lowerCAmelCase       ,				)
		return block_records







	def  _a   (  self      :							Tuple			)		-> str:
		"""simple docstring"""







		__lowercase             =  RealmRetriever(
		    block_records=self.get_dummy_block_records()       ,				tokenizer=self.get_tokenizer()       ,				)
		return retriever







	def  _a   (  self      :							Union[str, Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self.get_config()
		__lowercase             =  self.get_dummy_retriever()
		__lowercase             =  retriever.tokenizer

		__lowercase             =  np.array([0, 3]       ,				dtype="""long"""			)
		__lowercase             =  tokenizer(["""Test question"""]			).input_ids
		__lowercase             =  tokenizer(
		    ["""the fourth"""]       ,				add_special_tokens=_lowerCAmelCase       ,				return_token_type_ids=_lowerCAmelCase       ,				return_attention_mask=_lowerCAmelCase       ,				).input_ids
		__lowercase             =  config.reader_seq_len

		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  retriever(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				answer_ids=_lowerCAmelCase       ,				max_length=_lowerCAmelCase       ,				return_tensors="""np"""			)

		self.assertEqual(len(_lowerCAmelCase			)       ,				2			)
		self.assertEqual(len(_lowerCAmelCase			)       ,				2			)
		self.assertEqual(len(_lowerCAmelCase			)       ,				2			)
		self.assertEqual(concat_inputs.input_ids.shape       ,				(2, 10)			)
		self.assertEqual(concat_inputs.attention_mask.shape       ,				(2, 10)			)
		self.assertEqual(concat_inputs.token_type_ids.shape       ,				(2, 10)			)
		self.assertEqual(concat_inputs.special_tokens_mask.shape       ,				(2, 10)			)
		self.assertEqual(
		    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]			)       ,				["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""]       ,				)
		self.assertEqual(
		    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]			)       ,				["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""]       ,				)







	def  _a   (  self      :							Optional[Any]			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.get_config()
		__lowercase             =  self.get_dummy_retriever()
		__lowercase             =  retriever.tokenizer

		__lowercase             =  np.array([0, 3, 5]       ,				dtype="""long"""			)
		__lowercase             =  tokenizer(["""Test question"""]			).input_ids
		__lowercase             =  tokenizer(
		    ["""the fourth""", """longer longer"""]       ,				add_special_tokens=_lowerCAmelCase       ,				return_token_type_ids=_lowerCAmelCase       ,				return_attention_mask=_lowerCAmelCase       ,				).input_ids
		__lowercase             =  config.reader_seq_len

		__lowercase       ,						__lowercase       ,						__lowercase       ,						__lowercase             =  retriever(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				answer_ids=_lowerCAmelCase       ,				max_length=_lowerCAmelCase       ,				return_tensors="""np"""			)

		self.assertEqual([False, True, True]       ,				_lowerCAmelCase			)
		self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]       ,				_lowerCAmelCase			)
		self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							int			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.get_dummy_retriever()
		retriever.save_pretrained(os.path.join(self.tmpdirname       ,				"""realm_block_records"""			)			)

		# Test local path
		__lowercase             =  retriever.from_pretrained(os.path.join(self.tmpdirname       ,				"""realm_block_records"""			)			)
		self.assertEqual(retriever.block_records[0]       ,				B"""This is the first record"""			)

		# Test mocked remote path
		with patch("""transformers.models.realm.retrieval_realm.hf_hub_download"""			) as mock_hf_hub_download:
			__lowercase             =  os.path.join(
			    os.path.join(self.tmpdirname       ,				"""realm_block_records"""			)       ,				_REALM_BLOCK_RECORDS_FILENAME			)
			__lowercase             =  RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa"""			)

		self.assertEqual(retriever.block_records[0]       ,				B"""This is the first record"""			)









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			if i + 1 == timesteps.shape[0]:
				__lowercase             =  None
			else:
				__lowercase             =  timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(
			    _lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass

