
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class        lowercase      (	a__						,     unittest.TestCase     ):
		__lowercase : int			  =       LEDTokenizer
		__lowercase : List[Any]			  =       LEDTokenizerFast
		__lowercase : Dict			  =       True



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				super().setUp()
				UpperCamelCase						=  [
				    '''l''',
				    '''o''',
				    '''w''',
				    '''e''',
				    '''r''',
				    '''s''',
				    '''t''',
				    '''i''',
				    '''d''',
				    '''n''',
				    '''\u0120''',
				    '''\u0120l''',
				    '''\u0120n''',
				    '''\u0120lo''',
				    '''\u0120low''',
				    '''er''',
				    '''\u0120lowest''',
				    '''\u0120newer''',
				    '''\u0120wider''',
				    '''<unk>''',
				]
				UpperCamelCase						=  dict(zip(lowercase__       ,   range(len(lowercase__ ) ) ) )
				UpperCamelCase						=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
				UpperCamelCase						=  {'''unk_token''': '''<unk>'''}

				UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file'] )
				UpperCamelCase						=  os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['merges_file'] )
				with open(self.vocab_file       ,   'w'       ,   encoding='utf-8' ) as fp:
						fp.write(json.dumps(lowercase__ ) + '\n' )
				with open(self.merges_file       ,   'w'       ,   encoding='utf-8' ) as fp:
						fp.write('\n'.join(lowercase__ ) )



		def __UpperCamelCase				(				self       ,   **A_ )						->      str:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map )
				return self.tokenizer_class.from_pretrained(self.tmpdirname       ,   **lowercase__ )



		def __UpperCamelCase				(				self       ,   **A_ )						->      List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map )
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,   **lowercase__ )



		def __UpperCamelCase				(				self       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				return "lower newer", "lower newer"



		@cached_property
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				return LEDTokenizer.from_pretrained('allenai/led-base-16384' )



		@cached_property
		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				return LEDTokenizerFast.from_pretrained('allenai/led-base-16384' )



		@require_torch
		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				UpperCamelCase						=  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						UpperCamelCase						=  tokenizer(lowercase__       ,   max_length=len(lowercase__ )       ,   padding=lowercase__       ,   return_tensors='pt' )
						self.assertIsInstance(lowercase__       ,   lowercase__ )

						self.assertEqual((2, 9)       ,   batch.input_ids.shape )
						self.assertEqual((2, 9)       ,   batch.attention_mask.shape )
						UpperCamelCase						=  batch.input_ids.tolist()[0]
						self.assertListEqual(lowercase__       ,   lowercase__ )



		@require_torch
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						UpperCamelCase						=  tokenizer(lowercase__       ,   padding=lowercase__       ,   return_tensors='pt' )
						self.assertIn('input_ids'       ,   lowercase__ )
						self.assertIn('attention_mask'       ,   lowercase__ )
						self.assertNotIn('labels'       ,   lowercase__ )
						self.assertNotIn('decoder_attention_mask'       ,   lowercase__ )



		@require_torch
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  [
				    '''Summary of the text.''',
				    '''Another summary.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						UpperCamelCase						=  tokenizer(text_target=lowercase__       ,   max_length=32       ,   padding='max_length'       ,   return_tensors='pt' )
						self.assertEqual(32       ,   targets['input_ids'].shape[1] )



		@require_torch
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""





				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						UpperCamelCase						=  tokenizer(
						    ['I am a small frog' * 1_024, 'I am a small frog']       ,   padding=lowercase__       ,   truncation=lowercase__       ,   return_tensors='pt' )
						self.assertIsInstance(lowercase__       ,   lowercase__ )
						self.assertEqual(batch.input_ids.shape       ,   (2, 5_122) )



		@require_torch
		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  ['''A long paragraph for summarization.''']
				UpperCamelCase						=  [
				    '''Summary of the text.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						UpperCamelCase						=  tokenizer(lowercase__       ,   return_tensors='pt' )
						UpperCamelCase						=  tokenizer(text_target=lowercase__       ,   return_tensors='pt' )
						UpperCamelCase						=  inputs['''input_ids''']
						UpperCamelCase						=  targets['''input_ids''']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )



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





				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						UpperCamelCase						=  ['''Summary of the text.''', '''Another summary.''']
						UpperCamelCase						=  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

						UpperCamelCase						=  tokenizer(lowercase__       ,   padding=lowercase__ )
						UpperCamelCase						=  [[0] * len(lowercase__ ) for x in encoded_output['''input_ids''']]
						UpperCamelCase						=  tokenizer.pad(lowercase__ )
						self.assertSequenceEqual(outputs['global_attention_mask']       ,   lowercase__ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				pass



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





				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
								UpperCamelCase						=  self.rust_tokenizer_class.from_pretrained(lowercase__       ,   **lowercase__ )
								UpperCamelCase						=  self.tokenizer_class.from_pretrained(lowercase__       ,   **lowercase__ )
								UpperCamelCase						=  '''A, <mask> AllenNLP sentence.'''
								UpperCamelCase						=  tokenizer_r.encode_plus(lowercase__       ,   add_special_tokens=lowercase__       ,   return_token_type_ids=lowercase__ )
								UpperCamelCase						=  tokenizer_p.encode_plus(lowercase__       ,   add_special_tokens=lowercase__       ,   return_token_type_ids=lowercase__ )
								self.assertEqual(sum(tokens_r['token_type_ids'] )       ,   sum(tokens_p['token_type_ids'] ) )
								self.assertEqual(
								    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )       ,   sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )       ,   )

								UpperCamelCase						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
								UpperCamelCase						=  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
								self.assertSequenceEqual(tokens_p['input_ids']       ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
								self.assertSequenceEqual(tokens_r['input_ids']       ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

								self.assertSequenceEqual(
								    lowercase__       ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
								self.assertSequenceEqual(
								    lowercase__       ,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,   [0.002, 0.02, 0.2, 2] ):
						self.check_over_configs(beta_start=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

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

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

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

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 258.9606 ) < 1e-2
				assert abs(result_mean.item() - 0.3372 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

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

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

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

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 202.0296 ) < 1e-2
				assert abs(result_mean.item() - 0.2631 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

				for i, timestep in enumerate(A_ ):
						if i == len(A_ ) - 1:
								UpperCamelCase						=  -1
						else:
								UpperCamelCase						=  timesteps[i + 1]

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 51, 0]

				with self.assertRaises(A_       ,   msg='`custom_timesteps` must be in descending order.' ):
						scheduler.set_timesteps(timesteps=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

				with self.assertRaises(A_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
						scheduler.set_timesteps(num_inference_steps=A_       ,   timesteps=A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    A_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
						scheduler.set_timesteps(timesteps=A_ )
