import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_snake_case						(						a__				):
					lowerCamelCase__:		int  =     (LMSDiscreteScheduler,)
					lowerCamelCase__:		Any  =     10
					def    _lowerCamelCase (  self:				List[Any]	,  **__lowerCamelCase:				str     )    ->				Any:
								__UpperCAmelCase						:			Union[str, Any]         =   {
								    "num_train_timesteps": 11_00,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**__A     )
								return config
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Tuple:







								for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=__A     )
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Tuple:







								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]	,  [0.00_02, 0.0_02, 0.02]     ):
											self.check_over_configs(beta_start=__A	,  beta_end=__A     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				str:







								for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=__A     )
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				List[Any]:







								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=__A     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				List[Any]:







								for t in [0, 5_00, 8_00]:
											self.check_over_forward(time_step=__A     )
					def    _lowerCamelCase (  self:				Any     )    ->				Any:
								__UpperCAmelCase						:			Dict         =   self.scheduler_classes[0]
								__UpperCAmelCase						:			List[Any]         =   self.get_scheduler_config()
								__UpperCAmelCase						:			Any         =   scheduler_class(**__A     )

								scheduler.set_timesteps(self.num_inference_steps     )

								__UpperCAmelCase						:			Union[str, Any]         =   self.dummy_model()
								__UpperCAmelCase						:			Dict         =   self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps     ):
											__UpperCAmelCase						:			Tuple         =   scheduler.scale_model_input(__A	,  __A     )

											__UpperCAmelCase						:			Optional[int]         =   model(__A	,  __A     )

											__UpperCAmelCase						:			Dict         =   scheduler.step(__A	,  __A	,  __A     )
											__UpperCAmelCase						:			List[Any]         =   output.prev_sample

								__UpperCAmelCase						:			List[Any]         =   torch.sum(torch.abs(__A     )     )
								__UpperCAmelCase						:			str         =   torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 10_06.3_88     ) < 1e-2
								assert abs(result_mean.item() - 1.31     ) < 1e-3
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				Any:
								__UpperCAmelCase						:			str         =   self.scheduler_classes[0]
								__UpperCAmelCase						:			Optional[int]         =   self.get_scheduler_config(prediction_type="v_prediction"     )
								__UpperCAmelCase						:			Union[str, Any]         =   scheduler_class(**__A     )

								scheduler.set_timesteps(self.num_inference_steps     )

								__UpperCAmelCase						:			int         =   self.dummy_model()
								__UpperCAmelCase						:			str         =   self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps     ):
											__UpperCAmelCase						:			Any         =   scheduler.scale_model_input(__A	,  __A     )

											__UpperCAmelCase						:			Optional[int]         =   model(__A	,  __A     )

											__UpperCAmelCase						:			List[Any]         =   scheduler.step(__A	,  __A	,  __A     )
											__UpperCAmelCase						:			int         =   output.prev_sample

								__UpperCAmelCase						:			List[str]         =   torch.sum(torch.abs(__A     )     )
								__UpperCAmelCase						:			List[str]         =   torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 0.00_17     ) < 1e-2
								assert abs(result_mean.item() - 2.2676e-06     ) < 1e-3
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
								__UpperCAmelCase						:			Dict         =   self.scheduler_classes[0]
								__UpperCAmelCase						:			Tuple         =   self.get_scheduler_config()
								__UpperCAmelCase						:			Optional[Any]         =   scheduler_class(**__A     )

								scheduler.set_timesteps(self.num_inference_steps	,  device=__A     )

								__UpperCAmelCase						:			List[str]         =   self.dummy_model()
								__UpperCAmelCase						:			Optional[Any]         =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__UpperCAmelCase						:			Union[str, Any]         =   sample.to(__A     )

								for i, t in enumerate(scheduler.timesteps     ):
											__UpperCAmelCase						:			str         =   scheduler.scale_model_input(__A	,  __A     )

											__UpperCAmelCase						:			Any         =   model(__A	,  __A     )

											__UpperCAmelCase						:			int         =   scheduler.step(__A	,  __A	,  __A     )
											__UpperCAmelCase						:			List[str]         =   output.prev_sample

								__UpperCAmelCase						:			str         =   torch.sum(torch.abs(__A     )     )
								__UpperCAmelCase						:			Union[str, Any]         =   torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 10_06.3_88     ) < 1e-2
								assert abs(result_mean.item() - 1.31     ) < 1e-3





					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Tuple:
								__UpperCAmelCase						:			Union[str, Any]         =   self.scheduler_classes[0]
								__UpperCAmelCase						:			Dict         =   self.get_scheduler_config()
								__UpperCAmelCase						:			Dict         =   scheduler_class(**__A	,  use_karras_sigmas=__A     )

								scheduler.set_timesteps(self.num_inference_steps	,  device=__A     )

								__UpperCAmelCase						:			str         =   self.dummy_model()
								__UpperCAmelCase						:			Any         =   self.dummy_sample_deter.to(__A     ) * scheduler.init_noise_sigma
								__UpperCAmelCase						:			Union[str, Any]         =   sample.to(__A     )

								for t in scheduler.timesteps:
											__UpperCAmelCase						:			str         =   scheduler.scale_model_input(__A	,  __A     )

											__UpperCAmelCase						:			Optional[int]         =   model(__A	,  __A     )

											__UpperCAmelCase						:			List[str]         =   scheduler.step(__A	,  __A	,  __A     )
											__UpperCAmelCase						:			Dict         =   output.prev_sample

								__UpperCAmelCase						:			List[str]         =   torch.sum(torch.abs(__A     )     )
								__UpperCAmelCase						:			str         =   torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 38_12.99_27     ) < 2e-2
								assert abs(result_mean.item() - 4.96_48     ) < 1e-3





import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
	from tokenizers import Tokenizer
	from tokenizers.models import WordPiece




class 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				int     )    ->				List[Any]:
												__UpperCAmelCase						:			List[str]         =   list(tokenizer_class.max_model_input_sizes.keys()     )
												for model_name in sa_models[:1]:
																			__UpperCAmelCase						:			Any         =   tokenizer_class.from_pretrained(__lowerCamelCase     )
																			self.assertIsNotNone(__lowerCamelCase     )
																			self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )
																			self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )

																			for special_tok in tokenizer.all_special_tokens:
																										self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )
																										__UpperCAmelCase						:			str         =   tokenizer.convert_tokens_to_ids(__lowerCamelCase     )
																										self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				BatchEncoding	,  __lowerCamelCase:				Optional[Callable] = None     )    ->				Dict:
												__UpperCAmelCase						:			int         =   pickle.dumps(__lowerCamelCase     )
												self.assertIsNotNone(__lowerCamelCase     )

												__UpperCAmelCase						:			Dict         =   pickle.loads(__lowerCamelCase     )

												# Ensure is_fast is correctly restored
												self.assertEqual(be_restored.is_fast	,  be_original.is_fast     )

												# Ensure encodings are potentially correctly restored
												if be_original.is_fast:
																			self.assertIsNotNone(be_restored.encodings     )
												else:
																			self.assertIsNone(be_restored.encodings     )

												# Ensure the keys are the same
												for original_v, restored_v in zip(be_original.values()	,  be_restored.values()     ):
																			if equal_op:
																										self.assertTrue(equal_op(__lowerCamelCase	,  __lowerCamelCase     )     )
																			else:
																										self.assertEqual(__lowerCamelCase	,  __lowerCamelCase     )
					@slow
					def    _lowerCamelCase (  self:				str     )    ->				Optional[Any]:
												self.check_tokenizer_from_pretrained(__lowerCamelCase     )
					def    _lowerCamelCase (  self:				str     )    ->				Dict:
												self.assertEqual(TensorType("tf"     )	,  TensorType.TENSORFLOW     )
												self.assertEqual(TensorType("pt"     )	,  TensorType.PYTORCH     )
												self.assertEqual(TensorType("np"     )	,  TensorType.NUMPY     )
					@require_tokenizers
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				int:
												import numpy as np

												__UpperCAmelCase						:			str         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Any         =   BertTokenizerFast.from_pretrained("bert-base-cased"     )

												# Python no tensor
												with self.subTest("BatchEncoding (Python, return_tensors=None)"     ):
																			self.assert_dump_and_restore(tokenizer_p("Small example to encode"     )     )

												with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_p("Small example to encode"	,  return_tensors=TensorType.NUMPY     )	,  np.array_equal     )

												with self.subTest("BatchEncoding (Rust, return_tensors=None)"     ):
																			self.assert_dump_and_restore(tokenizer_r("Small example to encode"     )     )

												with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_r("Small example to encode"	,  return_tensors=TensorType.NUMPY     )	,  np.array_equal     )
					@require_tf
					@require_tokenizers
					def    _lowerCamelCase (  self:				Tuple     )    ->				str:
												import tensorflow as tf

												def tf_array_equals(__lowerCamelCase:				Dict	,  __lowerCamelCase:				Tuple     ):
																			return tf.reduce_all(tf.equal(__lowerCamelCase	,  __lowerCamelCase     )     )

												__UpperCAmelCase						:			List[str]         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Optional[int]         =   BertTokenizerFast.from_pretrained("bert-base-cased"     )

												with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_p("Small example to encode"	,  return_tensors=TensorType.TENSORFLOW     )	,  __lowerCamelCase     )

												with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_r("Small example to encode"	,  return_tensors=TensorType.TENSORFLOW     )	,  __lowerCamelCase     )
					@require_torch
					@require_tokenizers
					def    _lowerCamelCase (  self:				Tuple     )    ->				Union[str, Any]:
												import torch

												__UpperCAmelCase						:			Optional[int]         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Optional[Any]         =   BertTokenizerFast.from_pretrained("bert-base-cased"     )

												with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_p("Small example to encode"	,  return_tensors=TensorType.PYTORCH     )	,  torch.equal     )

												with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_r("Small example to encode"	,  return_tensors=TensorType.PYTORCH     )	,  torch.equal     )
					@require_tokenizers
					def    _lowerCamelCase (  self:				int     )    ->				Any:
												__UpperCAmelCase						:			Tuple         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Tuple         =   BertTokenizerFast.from_pretrained("bert-base-cased"     )

												with self.subTest("Python Tokenizer"     ):
																			self.assertFalse(tokenizer_p("Small example to_encode"     ).is_fast     )

												with self.subTest("Rust Tokenizer"     ):
																			self.assertTrue(tokenizer_r("Small example to_encode"     ).is_fast     )
					@require_tokenizers
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				List[str]:
												__UpperCAmelCase						:			Tuple         =   BertTokenizerFast.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Tuple         =   tokenizer_r(["Test", "\xad", "test"]	,  is_split_into_words=__lowerCamelCase     )

												self.assertEqual(encoded.word_to_tokens(0     )	,  TokenSpan(start=1	,  end=2     )     )
												self.assertEqual(encoded.word_to_tokens(1     )	,  __lowerCamelCase     )
												self.assertEqual(encoded.word_to_tokens(2     )	,  TokenSpan(start=2	,  end=3     )     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[Any]:
												__UpperCAmelCase						:			str         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			str         =   batch.convert_to_tensors(tensor_type="np"     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (2, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (2,)     )
												# test converting the converted
												with CaptureStderr() as cs:
																			__UpperCAmelCase						:			Tuple         =   batch.convert_to_tensors(tensor_type="np"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			Optional[Any]         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			Any         =   batch.convert_to_tensors(tensor_type="np"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					@require_torch
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												__UpperCAmelCase						:			str         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			Union[str, Any]         =   batch.convert_to_tensors(tensor_type="pt"     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (2, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (2,)     )
												# test converting the converted
												with CaptureStderr() as cs:
																			__UpperCAmelCase						:			Optional[int]         =   batch.convert_to_tensors(tensor_type="pt"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			Optional[Any]         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			Union[str, Any]         =   batch.convert_to_tensors(tensor_type="pt"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					@require_tf
					def    _lowerCamelCase (  self:				Any     )    ->				Dict:
												__UpperCAmelCase						:			Any         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			Union[str, Any]         =   batch.convert_to_tensors(tensor_type="tf"     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (2, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (2,)     )
												# test converting the converted
												with CaptureStderr() as cs:
																			__UpperCAmelCase						:			Tuple         =   batch.convert_to_tensors(tensor_type="tf"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			Tuple         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			int         =   batch.convert_to_tensors(tensor_type="tf"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					@require_flax
					def    _lowerCamelCase (  self:				Any     )    ->				Tuple:
												__UpperCAmelCase						:			Optional[Any]         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			List[Any]         =   batch.convert_to_tensors(tensor_type="jax"     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (2, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (2,)     )
												# test converting the converted
												with CaptureStderr() as cs:
																			__UpperCAmelCase						:			Tuple         =   batch.convert_to_tensors(tensor_type="jax"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			str         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			str         =   batch.convert_to_tensors(tensor_type="jax"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[Any]         =   [{"input_ids": np.array([0, 1, 2]     )}, {"input_ids": np.array([0, 1, 2, 3]     )}]
												__UpperCAmelCase						:			Any         =   BertTokenizer.from_pretrained("bert-base-cased"     )

												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase     )
												self.assertTrue(isinstance(batch["input_ids"]	,  np.ndarray     )     )
												self.assertEqual(batch["input_ids"].tolist()	,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase	,  return_tensors="np"     )
												self.assertTrue(isinstance(batch["input_ids"]	,  np.ndarray     )     )
												self.assertEqual(batch["input_ids"].tolist()	,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
					@require_torch
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Optional[Any]:
												import torch

												__UpperCAmelCase						:			Any         =   [{"input_ids": torch.tensor([0, 1, 2]     )}, {"input_ids": torch.tensor([0, 1, 2, 3]     )}]
												__UpperCAmelCase						:			Any         =   BertTokenizer.from_pretrained("bert-base-cased"     )

												__UpperCAmelCase						:			str         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase     )
												self.assertTrue(isinstance(batch["input_ids"]	,  torch.Tensor     )     )
												self.assertEqual(batch["input_ids"].tolist()	,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase	,  return_tensors="pt"     )
												self.assertTrue(isinstance(batch["input_ids"]	,  torch.Tensor     )     )
												self.assertEqual(batch["input_ids"].tolist()	,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
					@require_tf
					def    _lowerCamelCase (  self:				int     )    ->				Union[str, Any]:
												import tensorflow as tf

												__UpperCAmelCase						:			Any         =   [{"input_ids": tf.constant([0, 1, 2]     )}, {"input_ids": tf.constant([0, 1, 2, 3]     )}]
												__UpperCAmelCase						:			List[str]         =   BertTokenizer.from_pretrained("bert-base-cased"     )

												__UpperCAmelCase						:			str         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase     )
												self.assertTrue(isinstance(batch["input_ids"]	,  tf.Tensor     )     )
												self.assertEqual(batch["input_ids"].numpy().tolist()	,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
												__UpperCAmelCase						:			Dict         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase	,  return_tensors="tf"     )
												self.assertTrue(isinstance(batch["input_ids"]	,  tf.Tensor     )     )
												self.assertEqual(batch["input_ids"].numpy().tolist()	,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]     )
					@require_tokenizers
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												__UpperCAmelCase						:			Optional[int]         =   Tokenizer(WordPiece(unk_token="[UNK]"     )     )
												PreTrainedTokenizerFast(tokenizer_object=__lowerCamelCase     )





					@require_tokenizers
					def    _lowerCamelCase (  self:				str     )    ->				Dict:
												__UpperCAmelCase						:			Tuple         =   Tokenizer(WordPiece(unk_token="[UNK]"     )     )
												with tempfile.TemporaryDirectory() as tmpdirname:
																			bert_tokenizer.save(os.path.join(__lowerCamelCase	,  "tokenizer.json"     )     )
																			PreTrainedTokenizerFast(tokenizer_file=os.path.join(__lowerCamelCase	,  "tokenizer.json"     )     )




