
"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				tempfile.mkdtemp()

          lowercase       :							Union[str, Any]									=				[
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          lowercase       :							Dict									=				os.path.join(self.tmpdirname      ,       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       :							Optional[int]									=				{
              "do_resize": True,
              "size": 20,
              "do_center_crop": True,
              "crop_size": 18,
              "do_normalize": True,
              "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
              "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
          }
          lowercase       :							Optional[int]									=				os.path.join(self.tmpdirname      ,       _a							)
          with open(self.image_processor_file      ,       "w"      ,       encoding="utf-8"							) as fp:
                 json.dump(_a      ,       _a							)
   def      __magic_name__     (     self      ,       **_a							):
          return BertTokenizer.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          return BertTokenizerFast.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self							):
          shutil.rmtree(self.tmpdirname							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				[np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta							)]
          lowercase       :							List[str]									=				[Image.fromarray(np.moveaxis(_a      ,       0      ,       -1							)							) for x in image_inputs]
          return image_inputs
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.get_tokenizer()
          lowercase       :							List[str]									=				self.get_rust_tokenizer()
          lowercase       :							Tuple									=				self.get_image_processor()

          lowercase       :							Optional[int]									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)
          processor_slow.save_pretrained(self.tmpdirname							)
          lowercase       :							List[str]									=				AlignProcessor.from_pretrained(self.tmpdirname      ,       use_fast=_a							)

          lowercase       :							Optional[Any]									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)
          processor_fast.save_pretrained(self.tmpdirname							)
          lowercase       :							int									=				AlignProcessor.from_pretrained(self.tmpdirname							)

          self.assertEqual(processor_slow.tokenizer.get_vocab()      ,       tokenizer_slow.get_vocab()							)
          self.assertEqual(processor_fast.tokenizer.get_vocab()      ,       tokenizer_fast.get_vocab()							)
          self.assertEqual(tokenizer_slow.get_vocab()      ,       tokenizer_fast.get_vocab()							)
          self.assertIsInstance(processor_slow.tokenizer      ,       _a							)
          self.assertIsInstance(processor_fast.tokenizer      ,       _a							)

          self.assertEqual(processor_slow.image_processor.to_json_string()      ,       image_processor.to_json_string()							)
          self.assertEqual(processor_fast.image_processor.to_json_string()      ,       image_processor.to_json_string()							)
          self.assertIsInstance(processor_slow.image_processor      ,       _a							)
          self.assertIsInstance(processor_fast.image_processor      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				AlignProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()							)
          processor.save_pretrained(self.tmpdirname							)

          lowercase       :							List[str]									=				self.get_tokenizer(bos_token="(BOS)"      ,       eos_token="(EOS)"							)
          lowercase       :							Dict									=				self.get_image_processor(do_normalize=_a      ,       padding_value=1.0							)

          lowercase       :							List[Any]									=				AlignProcessor.from_pretrained(
              self.tmpdirname      ,       bos_token="(BOS)"      ,       eos_token="(EOS)"      ,       do_normalize=_a      ,       padding_value=1.0							)

          self.assertEqual(processor.tokenizer.get_vocab()      ,       tokenizer_add_kwargs.get_vocab()							)
          self.assertIsInstance(processor.tokenizer      ,       _a							)

          self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string()							)
          self.assertIsInstance(processor.image_processor      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				self.get_image_processor()
          lowercase       :							List[Any]									=				self.get_tokenizer()

          lowercase       :							Tuple									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)

          lowercase       :							List[str]									=				self.prepare_image_inputs()

          lowercase       :							List[Any]									=				image_processor(_a      ,       return_tensors="np"							)
          lowercase       :							str									=				processor(images=_a      ,       return_tensors="np"							)

          for key in input_image_proc.keys():
                 self.assertAlmostEqual(input_image_proc[key].sum()      ,       input_processor[key].sum()      ,       delta=1E-2							)
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.get_image_processor()
          lowercase       :							List[Any]									=				self.get_tokenizer()

          lowercase       :							int									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)

          lowercase       :							Any									=				"lower newer"

          lowercase       :							Optional[Any]									=				processor(text=_a							)

          lowercase       :							Optional[int]									=				tokenizer(_a      ,       padding="max_length"      ,       max_length=64							)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key]							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.get_image_processor()
          lowercase       :							Union[str, Any]									=				self.get_tokenizer()

          lowercase       :							Tuple									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)

          lowercase       :							List[str]									=				"lower newer"
          lowercase       :							Dict									=				self.prepare_image_inputs()

          lowercase       :							int									=				processor(text=_a      ,       images=_a							)

          self.assertListEqual(list(inputs.keys()							)      ,       ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]							)

          # test if it raises when no input is passed
          with pytest.raises(_a							):
                 processor()
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				self.get_image_processor()
          lowercase       :							List[Any]									=				self.get_tokenizer()

          lowercase       :							str									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)

          lowercase       :							List[str]									=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase       :							Optional[Any]									=				processor.batch_decode(_a							)
          lowercase       :							Tuple									=				tokenizer.batch_decode(_a							)

          self.assertListEqual(_a      ,       _a							)

   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.get_image_processor()
          lowercase       :							Any									=				self.get_tokenizer()

          lowercase       :							Tuple									=				AlignProcessor(tokenizer=_a      ,       image_processor=_a							)

          lowercase       :							List[Any]									=				"lower newer"
          lowercase       :							List[str]									=				self.prepare_image_inputs()

          lowercase       :							int									=				processor(text=_a      ,       images=_a							)

          self.assertListEqual(list(inputs.keys()							)      ,       processor.model_input_names							)




"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				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_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

          lowercase       :							str									=				self.dummy_model()
          lowercase       :							Optional[int]									=				self.dummy_sample_deter
          lowercase       :							List[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				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
                 lowercase       :							List[str]									=				pred_prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							List[str]									=				self.dummy_sample_deter
          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							Optional[Any]									=				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
                 lowercase       :							Dict									=				pred_prev_sample

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

          for i, timestep in enumerate(_a							):
                 if i == len(_a							) - 1:
                        lowercase       :							Any									=				-1
                 else:
                        lowercase       :							Union[str, Any]									=				timesteps[i + 1]

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				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      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[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							)


