

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		List[Any]										=    (UnCLIPScheduler,)



      def 						UpperCamelCase_		(      self				:    int      , **A__				:    Optional[Any]		)   ->					Union[str, Any]:
        _snake_case						=							{
            '''num_train_timesteps''': 1000,
            '''variance_type''': '''fixed_small_log''',
            '''clip_sample''': True,
            '''clip_sample_range''': 1.0,
            '''prediction_type''': '''epsilon''',
        }

        config.update(**A__		)
        return config



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					List[str]:
        for timesteps in [1, 5, 100, 1000]:
          self.check_over_configs(num_train_timesteps=A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Any:
        for variance in ["fixed_small_log", "learned_range"]:
          self.check_over_configs(variance_type=A__		)



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					str:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        for clip_sample_range in [1, 5, 10, 20]:
          self.check_over_configs(clip_sample_range=A__		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        for prediction_type in ["epsilon", "sample"]:
          self.check_over_configs(prediction_type=A__		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Dict:
        for time_step in [0, 500, 999]:
          for prev_timestep in [None, 5, 100, 250, 500, 750]:
            if prev_timestep is not None and prev_timestep >= time_step:
              continue

            self.check_over_forward(time_step=A__      , prev_timestep=A__		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config(variance_type='''fixed_small_log'''		)
        _snake_case						=							scheduler_class(**A__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000e-10		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.054_9625		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.999_4987		)		) < 1e-5



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Dict:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config(variance_type='''learned_range'''		)
        _snake_case						=							scheduler_class(**A__		)

        _snake_case						=							0.5

        assert scheduler._get_variance(1      , predicted_variance=A__		) - -10.171_2790 < 1e-5
        assert scheduler._get_variance(487      , predicted_variance=A__		) - -5.799_8052 < 1e-5
        assert scheduler._get_variance(999      , predicted_variance=A__		) - -0.001_0011 < 1e-5



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					List[str]:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config()
        _snake_case						=							scheduler_class(**A__		)

        _snake_case						=							scheduler.timesteps

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

        for i, t in enumerate(A__		):
          # 1. predict noise residual
          _snake_case						=							model(A__      , A__		)

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

          _snake_case						=							pred_prev_sample

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 252.268_2495		) < 1e-2
        assert abs(result_mean.item() - 0.328_4743		) < 1e-3



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Tuple:
        _snake_case						=							self.scheduler_classes[0]
        _snake_case						=							self.get_scheduler_config()
        _snake_case						=							scheduler_class(**A__		)

        scheduler.set_timesteps(25		)

        _snake_case						=							scheduler.timesteps

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

        for i, t in enumerate(A__		):
          # 1. predict noise residual
          _snake_case						=							model(A__      , A__		)

          if i + 1 == timesteps.shape[0]:
            _snake_case						=							None
          else:
            _snake_case						=							timesteps[i + 1]

          # 2. predict previous mean of sample x_t-1
          _snake_case						=							scheduler.step(
              A__      , A__      , A__      , prev_timestep=A__      , generator=A__		).prev_sample

          _snake_case						=							pred_prev_sample

        _snake_case						=							torch.sum(torch.abs(A__		)		)
        _snake_case						=							torch.mean(torch.abs(A__		)		)

        assert abs(result_sum.item() - 258.204_4983		) < 1e-2
        assert abs(result_mean.item() - 0.336_2038		) < 1e-3



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
        pass







      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[Any]:
        pass




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   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Union[str, Any]										=    CLIPTokenizer
      UpperCamelCase_      :		Optional[int]										=    CLIPTokenizerFast
      UpperCamelCase_      :		Dict										=    True
      UpperCamelCase_      :		Union[str, Any]										=    {}
      UpperCamelCase_      :		Optional[Any]										=    False



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Dict:
        super().setUp()

        # fmt: off
        _snake_case						=							['''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
        _snake_case						=							dict(zip(A__      , range(len(A__		)		)		)		)
        _snake_case						=							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
        _snake_case						=							{'''unk_token''': '''<unk>'''}

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



      def 						UpperCamelCase_		(      self				:    List[Any]      , **A__				:    int		)   ->					Union[str, Any]:
        kwargs.update(self.special_tokens_map		)
        return CLIPTokenizer.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Any      , **A__				:    Tuple		)   ->					Optional[Any]:
        kwargs.update(self.special_tokens_map		)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					str:
        _snake_case						=							'''lower newer'''
        _snake_case						=							'''lower newer'''
        return input_text, output_text



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[int]:
        _snake_case						=							CLIPTokenizer(self.vocab_file      , self.merges_file      , **self.special_tokens_map		)
        _snake_case						=							'''lower newer'''
        _snake_case						=							['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
        _snake_case						=							tokenizer.tokenize(A__		)
        self.assertListEqual(A__      , A__		)

        _snake_case						=							tokens + [tokenizer.unk_token]
        _snake_case						=							[10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A__		)      , A__		)



      @require_ftfy
      def 						UpperCamelCase_		(      self				:    Any		)   ->					Optional[int]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
            _snake_case						=							self.tokenizer_class.from_pretrained(A__      , **A__		)
            _snake_case						=							self.rust_tokenizer_class.from_pretrained(A__      , **A__		)

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

            self.assertListEqual(A__      , A__		)

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

            self.assertListEqual(A__      , A__		)

            # Test that the tokenization is identical on unicode of space type
            _snake_case						=							[
                '''\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:
              _snake_case						=							tokenizer_s.tokenize(A__		)
              _snake_case						=							tokenizer_r.tokenize(A__		)

              self.assertListEqual(A__      , A__		)

            # Test that the tokenization is identical on unicode of line break type
            _snake_case						=							[
                '''\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:
              _snake_case						=							tokenizer_s.tokenize(A__		)
              _snake_case						=							tokenizer_r.tokenize(A__		)

              self.assertListEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					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})"""		):
            _snake_case						=							'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
            _snake_case						=							f"""{text_of_1_token} {text_of_1_token}"""

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

            _snake_case						=							f""" {text}"""

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



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					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(A__		) 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				:    Dict		)   ->					Union[str, Any]:
        super().test_tokenization_python_rust_equals()







      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        # CLIP always lower cases letters
        pass

