
"""simple docstring"""





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 A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		LEDTokenizer
  SCREAMING_SNAKE_CASE__			:						Dict       =		LEDTokenizerFast
  SCREAMING_SNAKE_CASE__			:						List[str]       =		True







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()
     UpperCAmelCase_					:					List[Any]			     =    [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     UpperCAmelCase_					:					Union[str, Any]			     =    dict(zip(lowercase_				,   range(len(lowercase_  )  )  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     UpperCAmelCase_					:					Union[str, Any]			     =    {"unk_token": "<unk>"}

     UpperCAmelCase_					:					Any			     =    os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["vocab_file"]  )
     UpperCAmelCase_					:					Union[str, Any]			     =    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				,   **lowercase_  ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map  )
     return self.tokenizer_class.from_pretrained(self.tmpdirname				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map  )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     return "lower newer", "lower newer"







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     UpperCAmelCase_					:					Dict			     =    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					int			     =    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_					:					str			     =    batch.input_ids.tolist()[0]
        self.assertListEqual(lowercase_				,   lowercase_  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					str			     =    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  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					List[Any]			     =    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  ):

     """simple docstring"""
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					Tuple			     =    tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"]				,   padding=lowercase_				,   truncation=lowercase_				,   return_tensors="pt"  )
        self.assertIsInstance(lowercase_				,   lowercase_  )
        self.assertEqual(batch.input_ids.shape				,   (2, 5122)  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    ["A long paragraph for summarization."]
     UpperCAmelCase_					:					Optional[int]			     =    [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					Optional[Any]			     =    tokenizer(lowercase_				,   return_tensors="pt"  )
        UpperCAmelCase_					:					List[str]			     =    tokenizer(text_target=lowercase_				,   return_tensors="pt"  )
        UpperCAmelCase_					:					Optional[Any]			     =    inputs["input_ids"]
        UpperCAmelCase_					:					int			     =    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  ):

     """simple docstring"""
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					Dict			     =    ["Summary of the text.", "Another summary."]
        UpperCAmelCase_					:					Optional[Any]			     =    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        UpperCAmelCase_					:					Optional[Any]			     =    tokenizer(lowercase_				,   padding=lowercase_  )
        UpperCAmelCase_					:					Tuple			     =    [[0] * len(lowercase_  ) for x in encoded_output["input_ids"]]
        UpperCAmelCase_					:					Optional[Any]			     =    tokenizer.pad(lowercase_  )
        self.assertSequenceEqual(outputs["global_attention_mask"]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
           UpperCAmelCase_					:					Any			     =    self.rust_tokenizer_class.from_pretrained(lowercase_				,   **lowercase_  )
           UpperCAmelCase_					:					str			     =    self.tokenizer_class.from_pretrained(lowercase_				,   **lowercase_  )
           UpperCAmelCase_					:					Union[str, Any]			     =    "A, <mask> AllenNLP sentence."
           UpperCAmelCase_					:					List[Any]			     =    tokenizer_r.encode_plus(lowercase_				,   add_special_tokens=lowercase_				,   return_token_type_ids=lowercase_  )
           UpperCAmelCase_					:					Dict			     =    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_					:					Optional[Any]			     =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
           UpperCAmelCase_					:					Optional[Any]			     =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
           self.assertSequenceEqual(tokens_p["input_ids"]				,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
           self.assertSequenceEqual(tokens_r["input_ids"]				,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

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




"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    10, 0.0

     UpperCAmelCase_					:					Optional[int]			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter

     scheduler.set_timesteps(lowercase_  )

     for t in scheduler.timesteps:
        UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(5  )
     assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,   [0.0_02, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.check_over_configs(thresholding=lowercase_  )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowercase_				,   prediction_type=lowercase_				,   sample_max_value=lowercase_				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 500]  ):
        self.check_over_forward(time_step=lowercase_				,   num_inference_steps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, eta in zip([1, 10, 49]				,   [0.0, 0.5, 1.0]  ):
        self.check_over_forward(time_step=lowercase_				,   eta=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420				,   400  ) - 0.1_47_71  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980				,   960  ) - 0.3_24_60  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487				,   486  ) - 0.0_09_79  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				,   998  ) - 0.02  )  ) < 1E-5







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    10, 0.0
     scheduler.set_timesteps(lowercase_  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.dummy_model()
     UpperCAmelCase_					:					List[str]			     =    self.dummy_sample_deter
     UpperCAmelCase_					:					Any			     =    self.dummy_sample_deter + 0.1
     UpperCAmelCase_					:					int			     =    self.dummy_sample_deter - 0.1

     UpperCAmelCase_					:					List[Any]			     =    samplea.shape[0]
     UpperCAmelCase_					:					int			     =    torch.stack([samplea, samplea, samplea]				,   dim=0  )
     UpperCAmelCase_					:					int			     =    torch.arange(lowercase_  )[0:3, None].repeat(1				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    model(samples.flatten(0				,   1  )				,   timesteps.flatten(0				,   1  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    scheduler.batch_step_no_noise(lowercase_				,   timesteps.flatten(0				,   1  )				,   samples.flatten(0				,   1  )				,   lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
     assert abs(result_mean.item() - 0.49_82  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.full_loop()

     UpperCAmelCase_					:					int			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					List[str]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(prediction_type="v_prediction"  )

     UpperCAmelCase_					:					str			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 52.53_02  ) < 1E-2
     assert abs(result_mean.item() - 0.06_84  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Tuple			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
     assert abs(result_mean.item() - 0.19_51  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					int			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
     assert abs(result_mean.item() - 0.19_41  ) < 1E-3


