





import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast


class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	0


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
               __A						:			List[str]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
               self.assertGreater(len(_UpperCAmelCase)						,     0)

          for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
               __A						:			Any								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     (GPTaTokenizer, GPTaTokenizerFast))
               self.assertGreater(len(_UpperCAmelCase)						,     0)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
          self.assertEqual(tokenizer.vocab_size						,     12)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (RobertaTokenizer, RobertaTokenizerFast))
          self.assertEqual(tokenizer.vocab_size						,     20)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	AutoConfig.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)
          # Check that tokenizer_type ≠ model_type
          __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     config=_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
          self.assertEqual(tokenizer.vocab_size						,     12)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with tempfile.TemporaryDirectory() as tmp_dir:
               shutil.copy('./tests/fixtures/vocab.txt'						,     os.path.join(_UpperCAmelCase						,     'vocab.txt'))

               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='bert'						,     use_fast=_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          with tempfile.TemporaryDirectory() as tmp_dir:
               shutil.copy('./tests/fixtures/vocab.json'						,     os.path.join(_UpperCAmelCase						,     'vocab.json'))
               shutil.copy('./tests/fixtures/merges.txt'						,     os.path.join(_UpperCAmelCase						,     'merges.txt'))

               __A						:			str								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='gpt2'						,     use_fast=_UpperCAmelCase)
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with tempfile.TemporaryDirectory() as tmp_dir:
               shutil.copy('./tests/fixtures/vocab.txt'						,     os.path.join(_UpperCAmelCase						,     'vocab.txt'))

               __A						:			List[str]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='bert')
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          with tempfile.TemporaryDirectory() as tmp_dir:
               shutil.copy('./tests/fixtures/vocab.json'						,     os.path.join(_UpperCAmelCase						,     'vocab.json'))
               shutil.copy('./tests/fixtures/merges.txt'						,     os.path.join(_UpperCAmelCase						,     'merges.txt'))

               __A						:			List[str]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     tokenizer_type='gpt2')
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with pytest.raises(_UpperCAmelCase):
               AutoTokenizer.from_pretrained('./'						,     tokenizer_type='xxx')


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
               __A						:			List[Any]								=	tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased')
               self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))

               if isinstance(_UpperCAmelCase						,     _UpperCAmelCase):
                    self.assertEqual(tokenizer.basic_tokenizer.do_lower_case						,     _UpperCAmelCase)
               else:
                    self.assertEqual(tokenizer.do_lower_case						,     _UpperCAmelCase)

               self.assertEqual(tokenizer.model_max_length						,     512)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
               with self.assertRaisesRegex(
                   _UpperCAmelCase						,     'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'						,     ):
                    __A						:			str								=	tokenizer_class.from_pretrained('julien-c/herlolip-not-exists')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	TOKENIZER_MAPPING.values()
          __A						:			Union[str, Any]								=	[]

          for slow_tok, fast_tok in tokenizers:
               if slow_tok is not None:
                    tokenizer_names.append(slow_tok.__name__)

               if fast_tok is not None:
                    tokenizer_names.append(fast_tok.__name__)

          for tokenizer_name in tokenizer_names:
               # must find the right class
               tokenizer_class_from_name(_UpperCAmelCase)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'						,     use_fast=_UpperCAmelCase)						,     _UpperCAmelCase)
          self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased')						,     _UpperCAmelCase)


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	AutoTokenizer.from_pretrained('distilbert-base-uncased'						,     do_lower_case=_UpperCAmelCase)
          __A						:			str								=	'Hello, world. How are you?'
          __A						:			List[str]								=	tokenizer.tokenize(_UpperCAmelCase)
          self.assertEqual('[UNK]'						,     tokens[0])

          __A						:			Dict								=	AutoTokenizer.from_pretrained('microsoft/mpnet-base'						,     do_lower_case=_UpperCAmelCase)
          __A						:			List[Any]								=	tokenizer.tokenize(_UpperCAmelCase)
          self.assertEqual('[UNK]'						,     tokens[0])


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config')
          self.assertEqual(type(_UpperCAmelCase)						,     _UpperCAmelCase)
          self.assertEqual(tokenizer.model_max_length						,     512)
          self.assertEqual(tokenizer.vocab_size						,     3_0000)
          self.assertEqual(tokenizer.unk_token						,     '[UNK]')
          self.assertEqual(tokenizer.padding_side						,     'right')
          self.assertEqual(tokenizer.truncation_side						,     'right')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          self.assertIsInstance(_UpperCAmelCase						,     (BertTokenizer, BertTokenizerFast))
          with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer.save_pretrained(_UpperCAmelCase)
               __A						:			Any								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)

          self.assertIsInstance(_UpperCAmelCase						,     tokenizer.__class__)
          self.assertEqual(tokenizera.vocab_size						,     12)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	AutoTokenizer.from_pretrained('ctrl')
          # There is no fast CTRL so this always gives us a slow tokenizer.
          self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	get_tokenizer_config('bert-base-cased')
          __A						:			Optional[int]								=	config.pop('_commit_hash'						,     _UpperCAmelCase)
          # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
          self.assertEqual(_UpperCAmelCase						,     {'do_lower_case': False})

          # This model does not have a tokenizer_config so we get back an empty dict.
          __A						:			Dict								=	get_tokenizer_config(_UpperCAmelCase)
          self.assertDictEqual(_UpperCAmelCase						,     {})

          # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
          __A						:			Any								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer.save_pretrained(_UpperCAmelCase)
               __A						:			Any								=	get_tokenizer_config(_UpperCAmelCase)

          # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
          self.assertEqual(config['tokenizer_class']						,     'BertTokenizer')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          try:
               AutoConfig.register('custom'						,     _UpperCAmelCase)

               AutoTokenizer.register(_UpperCAmelCase						,     slow_tokenizer_class=_UpperCAmelCase)
               # Trying to register something existing in the Transformers library will raise an error
               with self.assertRaises(_UpperCAmelCase):
                    AutoTokenizer.register(_UpperCAmelCase						,     slow_tokenizer_class=_UpperCAmelCase)

               __A						:			Optional[Any]								=	CustomTokenizer.from_pretrained(_UpperCAmelCase)
               with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						:			int								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          finally:
               if "custom" in CONFIG_MAPPING._extra_content:
                    del CONFIG_MAPPING._extra_content["custom"]
               if CustomConfig in TOKENIZER_MAPPING._extra_content:
                    del TOKENIZER_MAPPING._extra_content[CustomConfig]


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          try:
               AutoConfig.register('custom'						,     _UpperCAmelCase)

               # Can register in two steps
               AutoTokenizer.register(_UpperCAmelCase						,     slow_tokenizer_class=_UpperCAmelCase)
               self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						,     (CustomTokenizer, None))
               AutoTokenizer.register(_UpperCAmelCase						,     fast_tokenizer_class=_UpperCAmelCase)
               self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						,     (CustomTokenizer, CustomTokenizerFast))

               del TOKENIZER_MAPPING._extra_content[CustomConfig]
               # Can register in one step
               AutoTokenizer.register(
                   _UpperCAmelCase						,     slow_tokenizer_class=_UpperCAmelCase						,     fast_tokenizer_class=_UpperCAmelCase)
               self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						,     (CustomTokenizer, CustomTokenizerFast))

               # Trying to register something existing in the Transformers library will raise an error
               with self.assertRaises(_UpperCAmelCase):
                    AutoTokenizer.register(_UpperCAmelCase						,     fast_tokenizer_class=_UpperCAmelCase)

               # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
               # and that model does not have a tokenizer.json
               with tempfile.TemporaryDirectory() as tmp_dir:
                    __A						:			Optional[int]								=	BertTokenizerFast.from_pretrained(_UpperCAmelCase)
                    bert_tokenizer.save_pretrained(_UpperCAmelCase)
                    __A						:			Dict								=	CustomTokenizerFast.from_pretrained(_UpperCAmelCase)

               with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer.save_pretrained(_UpperCAmelCase)

                    __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

                    __A						:			Any								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
                    self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

          finally:
               if "custom" in CONFIG_MAPPING._extra_content:
                    del CONFIG_MAPPING._extra_content["custom"]
               if CustomConfig in TOKENIZER_MAPPING._extra_content:
                    del TOKENIZER_MAPPING._extra_content[CustomConfig]


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with self.assertRaises(_UpperCAmelCase):
               __A						:			List[str]								=	AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer')
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(_UpperCAmelCase):
               __A						:			Dict								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)

          __A						:			str								=	AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)
          self.assertTrue(tokenizer.special_attribute_present)
          # Test tokenizer can be reloaded.
          with tempfile.TemporaryDirectory() as tmp_dir:
               tokenizer.save_pretrained(_UpperCAmelCase)
               __A						:			Dict								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     trust_remote_code=_UpperCAmelCase)
          self.assertTrue(reloaded_tokenizer.special_attribute_present)

          if is_tokenizers_available():
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizerFast')
               self.assertEqual(reloaded_tokenizer.__class__.__name__						,     'NewTokenizerFast')

               # Test we can also load the slow version
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               self.assertTrue(tokenizer.special_attribute_present)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')
               # Test tokenizer can be reloaded.
               with tempfile.TemporaryDirectory() as tmp_dir:
                    tokenizer.save_pretrained(_UpperCAmelCase)
                    __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               self.assertEqual(reloaded_tokenizer.__class__.__name__						,     'NewTokenizer')
               self.assertTrue(reloaded_tokenizer.special_attribute_present)
          else:
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')
               self.assertEqual(reloaded_tokenizer.__class__.__name__						,     'NewTokenizer')


     @require_tokenizers
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          class 			SCREAMING_SNAKE_CASE   (a__							):
               lowerCAmelCase		      =	False




          class 			SCREAMING_SNAKE_CASE   (a__							):
               lowerCAmelCase		      =	NewTokenizer
               lowerCAmelCase		      =	False

          try:
               AutoConfig.register('custom'						,     _UpperCAmelCase)
               AutoTokenizer.register(_UpperCAmelCase						,     slow_tokenizer_class=_UpperCAmelCase)
               AutoTokenizer.register(_UpperCAmelCase						,     fast_tokenizer_class=_UpperCAmelCase)
               # If remote code is not set, the default is to use local
               __A						:			List[Any]								=	AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer')
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizerFast')
               self.assertFalse(tokenizer.special_attribute_present)
               __A						:			Dict								=	AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'						,     use_fast=_UpperCAmelCase)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')
               self.assertFalse(tokenizer.special_attribute_present)

               # If remote code is disabled, we load the local one.
               __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizerFast')
               self.assertFalse(tokenizer.special_attribute_present)
               __A						:			Any								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')
               self.assertFalse(tokenizer.special_attribute_present)

               # If remote is enabled, we load from the Hub
               __A						:			int								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizerFast')
               self.assertTrue(tokenizer.special_attribute_present)
               __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer'						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')
               self.assertTrue(tokenizer.special_attribute_present)

          finally:
               if "custom" in CONFIG_MAPPING._extra_content:
                    del CONFIG_MAPPING._extra_content["custom"]
               if CustomConfig in TOKENIZER_MAPPING._extra_content:
                    del TOKENIZER_MAPPING._extra_content[CustomConfig]


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	AutoTokenizer.from_pretrained(
              'hf-internal-testing/test_dynamic_tokenizer_legacy'						,     trust_remote_code=_UpperCAmelCase)
          self.assertTrue(tokenizer.special_attribute_present)
          if is_tokenizers_available():
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizerFast')

               # Test we can also load the slow version
               __A						:			int								=	AutoTokenizer.from_pretrained(
                   'hf-internal-testing/test_dynamic_tokenizer_legacy'						,     trust_remote_code=_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
               self.assertTrue(tokenizer.special_attribute_present)
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')
          else:
               self.assertEqual(tokenizer.__class__.__name__						,     'NewTokenizer')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with self.assertRaisesRegex(
              _UpperCAmelCase						,     'bert-base is not a local folder and is not a valid model identifier'):
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('bert-base')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with self.assertRaisesRegex(
              _UpperCAmelCase						,     R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     revision='aaaaaa')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
          with RequestCounter() as counter:
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert')
               self.assertEqual(counter.get_request_count						,     0)
               self.assertEqual(counter.head_request_count						,     1)
               self.assertEqual(counter.other_request_count						,     0)






'''simple docstring'''




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


lowercase__   :				Optional[int]      =   HfApi()

lowercase__   :				Dict      =   {}
# fmt: off
lowercase__   :				List[str]      =   torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
lowercase__   :				Tuple      =   torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
lowercase__   :				Optional[Any]      =   torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
lowercase__   :				List[Any]      =   torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
lowercase__   :				Dict      =   torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
lowercase__   :				Optional[int]      =   torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
lowercase__   :				List[Any]      =   torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
lowercase__   :				List[str]      =   torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
lowercase__   :				Dict      =   torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
lowercase__   :				Optional[int]      =   torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
lowercase__   :				List[str]      =   torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
lowercase__   :				Optional[int]      =   torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
lowercase__   :				int      =   torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
lowercase__   :				int      =   torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
lowercase__   :				List[Any]      =   torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

lowercase__   :				str      =   api.list_models(filter='''diffusers''')
for mod in models:
  if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
    lowercase__   :				int      =   '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

    print(f"""Started running {mod.modelId}!!!""")

    if mod.modelId.startswith('''CompVis'''):
      lowercase__   :				Optional[Any]      =   UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
    else:
      lowercase__   :				Tuple      =   UNetaDModel.from_pretrained(local_checkpoint)

    torch.manual_seed(0)
    random.seed(0)

    lowercase__   :				List[str]      =   torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
    lowercase__   :				int      =   torch.tensor([10] * noise.shape[0])
    with torch.no_grad():
      lowercase__   :				Tuple      =   model(noise, time_step).sample

    assert torch.allclose(
        logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
    )
    print(f"""{mod.modelId} has passed successfully!!!""")