





'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
    A__    =      {
        "num_train_timesteps": 10_00,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
    }

    config.update(**lowercase_							)
    return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
    A__    =      dict(self.forward_default_kwargs							)
    A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
    A__    =      self.dummy_sample
    A__    =      0.1 * sample
    A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

    for scheduler_class in self.scheduler_classes:
       A__    =      self.get_scheduler_config(**lowercase_							)
       A__    =      scheduler_class(**lowercase_							)
       scheduler.set_timesteps(lowercase_							)
       # copy over dummy past residuals
       A__    =      dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowercase_							)
          A__    =      scheduler_class.from_pretrained(lowercase_							)
          new_scheduler.set_timesteps(lowercase_							)
          # copy over dummy past residuals
          A__    =      dummy_past_residuals[:]

       A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
       A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

       assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

       A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
       A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

       assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
    pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
    A__    =      dict(self.forward_default_kwargs							)
    A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
    A__    =      self.dummy_sample
    A__    =      0.1 * sample
    A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

    for scheduler_class in self.scheduler_classes:
       A__    =      self.get_scheduler_config()
       A__    =      scheduler_class(**lowercase_							)
       scheduler.set_timesteps(lowercase_							)

       # copy over dummy past residuals (must be after setting timesteps)
       A__    =      dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowercase_							)
          A__    =      scheduler_class.from_pretrained(lowercase_							)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(lowercase_							)

          # copy over dummy past residual (must be after setting timesteps)
          A__    =      dummy_past_residuals[:]

       A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
       A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

       assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

       A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
       A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

       assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
    A__    =      self.scheduler_classes[0]
    A__    =      self.get_scheduler_config(**lowercase_							)
    A__    =      scheduler_class(**lowercase_							)

    A__    =      10
    A__    =      self.dummy_model()
    A__    =      self.dummy_sample_deter
    scheduler.set_timesteps(lowercase_							)

    for i, t in enumerate(scheduler.prk_timesteps							):
       A__    =      model(lowercase_						,	lowercase_							)
       A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

    for i, t in enumerate(scheduler.plms_timesteps							):
       A__    =      model(lowercase_						,	lowercase_							)
       A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

    return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
    A__    =      dict(self.forward_default_kwargs							)

    A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

    for scheduler_class in self.scheduler_classes:
       A__    =      self.get_scheduler_config()
       A__    =      scheduler_class(**lowercase_							)

       A__    =      self.dummy_sample
       A__    =      0.1 * sample

       if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
          scheduler.set_timesteps(lowercase_							)
       elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
          A__    =      num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
       A__    =      dummy_past_residuals[:]

       A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
       A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

       self.assertEqual(output_a.shape						,	sample.shape							)
       self.assertEqual(output_a.shape						,	output_a.shape							)

       A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
       A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

       self.assertEqual(output_a.shape						,	sample.shape							)
       self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
    for timesteps in [1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
    for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=lowercase_							)

    A__    =      self.scheduler_classes[0]
    A__    =      self.get_scheduler_config(steps_offset=1							)
    A__    =      scheduler_class(**lowercase_							)
    scheduler.set_timesteps(10							)
    assert torch.equal(
        scheduler.timesteps						,	torch.LongTensor(
            [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
       self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
    for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
    for t in [1, 5, 10]:
       self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
    for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
       self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
    # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
    A__    =      27

    for scheduler_class in self.scheduler_classes:
       A__    =      self.dummy_sample
       A__    =      0.1 * sample

       A__    =      self.get_scheduler_config()
       A__    =      scheduler_class(**lowercase_							)

       scheduler.set_timesteps(lowercase_							)

       # before power of 3 fix, would error on first step, so we only need to do two
       for i, t in enumerate(scheduler.prk_timesteps[:2]							):
          A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
    with self.assertRaises(lowercase_							):
       A__    =      self.scheduler_classes[0]
       A__    =      self.get_scheduler_config()
       A__    =      scheduler_class(**lowercase_							)

       scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
    A__    =      self.full_loop()
    A__    =      torch.sum(torch.abs(lowercase_							)							)
    A__    =      torch.mean(torch.abs(lowercase_							)							)

    assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
    assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
    A__    =      self.full_loop(prediction_type="v_prediction"							)
    A__    =      torch.sum(torch.abs(lowercase_							)							)
    A__    =      torch.mean(torch.abs(lowercase_							)							)

    assert abs(result_sum.item() - 67.39_86							) < 1E-2
    assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
    # We specify different beta, so that the first alpha is 0.99
    A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
    A__    =      torch.sum(torch.abs(lowercase_							)							)
    A__    =      torch.mean(torch.abs(lowercase_							)							)

    assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
    assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
    # We specify different beta, so that the first alpha is 0.99
    A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
    A__    =      torch.sum(torch.abs(lowercase_							)							)
    A__    =      torch.mean(torch.abs(lowercase_							)							)

    assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
    assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3










'''simple docstring'''

import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    CLIPTokenizer
 __lowercase       		=    CLIPTokenizerFast
 __lowercase       		=    True
 __lowercase       		=    {}
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     super().setUp()

     # fmt: off
     A__    =      ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
     # fmt: on
     A__    =      dict(zip(lowercase_						,	range(len(lowercase_							)							)							)							)
     A__    =      ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
     A__    =      {"unk_token": "<unk>"}

     A__    =      os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]							)
     A__    =      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				:Optional[int]						,	**lowercase_				:List[str]							)->							str:
     kwargs.update(self.special_tokens_map							)
     return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Optional[int]							)->							Tuple:
     kwargs.update(self.special_tokens_map							)
     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:List[Any]							)->							List[str]:
     A__    =      "lower newer"
     A__    =      "lower newer"
     return input_text, output_text
 def 					UpperCAmelCase_							(    self				:int							)->							List[str]:
     A__    =      CLIPTokenizer(self.vocab_file						,	self.merges_file						,	**self.special_tokens_map							)
     A__    =      "lower newer"
     A__    =      ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
     A__    =      tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      tokens + [tokenizer.unk_token]
     A__    =      [10, 2, 16, 9, 3, 2, 16, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)						,	lowercase_							)
 @require_ftfy
 def 					UpperCAmelCase_							(    self				:Dict							)->							str:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             A__    =      self.tokenizer_class.from_pretrained(lowercase_						,	**lowercase_							)
             A__    =      self.rust_tokenizer_class.from_pretrained(lowercase_						,	**lowercase_							)

             A__    =      "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
             A__    =      tokenizer_s.tokenize(lowercase_							)
             A__    =      tokenizer_r.tokenize(lowercase_							)

             self.assertListEqual(lowercase_						,	lowercase_							)

             # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
             # with Tilde) encoded in 2 different ways
             A__    =      "xa\u0303y" + " " + "x\xe3y"
             A__    =      tokenizer_s.tokenize(lowercase_							)
             A__    =      tokenizer_r.tokenize(lowercase_							)

             self.assertListEqual(lowercase_						,	lowercase_							)

             # Test that the tokenization is identical on unicode of space type
             A__    =      [
                 "\u0009",  # (horizontal tab, '\t')
                 "\u000B",  # (vertical tab)
                 "\u000C",  # (form feed)
                 "\u0020",  # (space, ' ')
                 "\u200E",  # (left-to-right mark):w
                 "\u200F",  # (right-to-left mark)
             ]
             for unicode_seq in spaces_unicodes:
                 A__    =      tokenizer_s.tokenize(lowercase_							)
                 A__    =      tokenizer_r.tokenize(lowercase_							)

                 self.assertListEqual(lowercase_						,	lowercase_							)

             # Test that the tokenization is identical on unicode of line break type
             A__    =      [
                 "\u000A",  # (line feed, '\n')
                 "\r\n",  # (carriage return and line feed, '\r\n')
                 "\u000D",  # (carriage return, '\r')
                 "\r",  # (carriage return, '\r')
                 "\u000D",  # (carriage return, '\r')
                 "\u2028",  # (line separator)
                 "\u2029",  # (paragraph separator)
                 # "\u0085", # (next line)
             ]

             # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
             # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
             # space (and thus into an empty list).

             for unicode_seq in line_break_unicodes:
                 A__    =      tokenizer_s.tokenize(lowercase_							)
                 A__    =      tokenizer_r.tokenize(lowercase_							)

                 self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             A__    =      "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
             A__    =      F"{text_of_1_token} {text_of_1_token}"

             A__    =      self.rust_tokenizer_class.from_pretrained(
                 lowercase_						,	use_fast=lowercase_						,	)
             A__    =      tokenizer_r(lowercase_						,	return_offsets_mapping=lowercase_						,	add_special_tokens=lowercase_							)
             self.assertEqual(encoding.offset_mapping[0]						,	(0, len(lowercase_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]						,	(len(lowercase_							) + 1, len(lowercase_							) + 1 + len(lowercase_							))						,	)

             A__    =      F" {text}"

             A__    =      self.rust_tokenizer_class.from_pretrained(
                 lowercase_						,	use_fast=lowercase_						,	)
             A__    =      tokenizer_r(lowercase_						,	return_offsets_mapping=lowercase_						,	add_special_tokens=lowercase_							)
             self.assertEqual(encoding.offset_mapping[0]						,	(1, 1 + len(lowercase_							))							)
             self.assertEqual(
                 encoding.offset_mapping[1]						,	(1 + len(lowercase_							) + 1, 1 + len(lowercase_							) + 1 + len(lowercase_							))						,	)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     # Test related to the breaking change introduced in transformers v4.17.0
     # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
     with self.assertRaises(lowercase_							) as context:
         self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"							)

     self.assertTrue(
         context.exception.args[0].startswith(
             "The `backend_tokenizer` provided does not match the expected format."							)							)
 @require_ftfy
 def 					UpperCAmelCase_							(    self				:str							)->							Any:
     super().test_tokenization_python_rust_equals()



 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Union[str, Any]:
     # CLIP always lower cases letters
     pass



