



"""simple docstring"""




import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     FlaubertForMultipleChoice,
     FlaubertForQuestionAnswering,
     FlaubertForQuestionAnsweringSimple,
     FlaubertForSequenceClassification,
     FlaubertForTokenClassification,
     FlaubertModel,
     FlaubertWithLMHeadModel,
 )
 from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class       UpperCAmelCase_    (							__lowercase		):



      def __init__(	self	:      Union[str, Any]	,      A	:      Optional[Any]	,      A	:      Any=1_3	,      A	:      Union[str, Any]=7	,      A	:      List[str]=True	,      A	:      List[str]=True	,      A	:      List[str]=True	,      A	:      List[str]=True	,      A	:      List[Any]=True	,      A	:      Optional[int]=False	,      A	:      Any=False	,      A	:      int=False	,      A	:      Optional[Any]=2	,      A	:      Any=9_9	,      A	:      str=0	,      A	:      Union[str, Any]=3_2	,      A	:      List[Any]=5	,      A	:      Tuple=4	,      A	:      List[str]=0.1	,      A	:      Union[str, Any]=0.1	,      A	:      int=5_1_2	,      A	:      Union[str, Any]=1_2	,      A	:      List[str]=2	,      A	:      int=0.02	,      A	:      Optional[Any]=3	,      A	:      Any=4	,      A	:      Optional[int]="last"	,      A	:      Any=None	,      A	:      Dict=None	,      ):
           _UpperCAmelCase     :      Union[str, Any]														=       parent
           _UpperCAmelCase     :      Union[str, Any]														=       batch_size
           _UpperCAmelCase     :      str														=       seq_length
           _UpperCAmelCase     :      Union[str, Any]														=       is_training
           _UpperCAmelCase     :      Tuple														=       use_input_lengths
           _UpperCAmelCase     :      Optional[int]														=       use_token_type_ids
           _UpperCAmelCase     :      str														=       use_labels
           _UpperCAmelCase     :      Optional[Any]														=       gelu_activation
           _UpperCAmelCase     :      List[Any]														=       sinusoidal_embeddings
           _UpperCAmelCase     :      Dict														=       causal
           _UpperCAmelCase     :      int														=       asm
           _UpperCAmelCase     :      List[str]														=       n_langs
           _UpperCAmelCase     :      Optional[int]														=       vocab_size
           _UpperCAmelCase     :      str														=       n_special
           _UpperCAmelCase     :      List[Any]														=       hidden_size
           _UpperCAmelCase     :      List[Any]														=       num_hidden_layers
           _UpperCAmelCase     :      Optional[int]														=       num_attention_heads
           _UpperCAmelCase     :      Optional[int]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      int														=       max_position_embeddings
           _UpperCAmelCase     :      List[str]														=       type_vocab_size
           _UpperCAmelCase     :      int														=       type_sequence_label_size
           _UpperCAmelCase     :      Optional[int]														=       initializer_range
           _UpperCAmelCase     :      Any														=       num_labels
           _UpperCAmelCase     :      List[str]														=       num_choices
           _UpperCAmelCase     :      Union[str, Any]														=       summary_type
           _UpperCAmelCase     :      Dict														=       use_proj
           _UpperCAmelCase     :      Tuple														=       scope



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)
           _UpperCAmelCase     :      List[str]														=       random_attention_mask([self.batch_size, self.seq_length]		)

           _UpperCAmelCase     :      str														=       None
           if self.use_input_lengths:
                _UpperCAmelCase     :      List[Any]														=       (
                    ids_tensor([self.batch_size]	,      vocab_size=2		) + self.seq_length - 2
                )  # small variation of seq_length

           _UpperCAmelCase     :      List[str]														=       None
           if self.use_token_type_ids:
                _UpperCAmelCase     :      Optional[int]														=       ids_tensor([self.batch_size, self.seq_length]	,      self.n_langs		)

           _UpperCAmelCase     :      List[str]														=       None
           _UpperCAmelCase     :      Optional[Any]														=       None
           _UpperCAmelCase     :      Any														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Dict														=       ids_tensor([self.batch_size]	,      self.type_sequence_label_size		)
                _UpperCAmelCase     :      Dict														=       ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels		)
                _UpperCAmelCase     :      List[str]														=       ids_tensor([self.batch_size]	,      2		).float()
                _UpperCAmelCase     :      Optional[int]														=       ids_tensor([self.batch_size]	,      self.num_choices		)

           _UpperCAmelCase     :      str														=       self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_lengths,
               sequence_labels,
               token_labels,
               is_impossible_labels,
               choice_labels,
               input_mask,
           )



      def 		snake_case_   (	self	:      str		):
           return FlaubertConfig(
               vocab_size=self.vocab_size	,      n_special=self.n_special	,      emb_dim=self.hidden_size	,      n_layers=self.num_hidden_layers	,      n_heads=self.num_attention_heads	,      dropout=self.hidden_dropout_prob	,      attention_dropout=self.attention_probs_dropout_prob	,      gelu_activation=self.gelu_activation	,      sinusoidal_embeddings=self.sinusoidal_embeddings	,      asm=self.asm	,      causal=self.causal	,      n_langs=self.n_langs	,      max_position_embeddings=self.max_position_embeddings	,      initializer_range=self.initializer_range	,      summary_type=self.summary_type	,      use_proj=self.use_proj	,      )



      def 		snake_case_   (	self	:      str	,      A	:      Union[str, Any]	,      A	:      Optional[Any]	,      A	:      str	,      A	:      Tuple	,      A	:      List[str]	,      A	:      List[Any]	,      A	:      Any	,      A	:      str	,      A	:      Optional[int]	,      ):
           _UpperCAmelCase     :      List[Any]														=       FlaubertModel(config=_A		)
           model.to(_A		)
           model.eval()
           _UpperCAmelCase     :      Optional[Any]														=       model(_A	,      lengths=_A	,      langs=_A		)
           _UpperCAmelCase     :      Dict														=       model(_A	,      langs=_A		)
           _UpperCAmelCase     :      List[str]														=       model(_A		)
           self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)		)



      def 		snake_case_   (	self	:      Tuple	,      A	:      List[Any]	,      A	:      str	,      A	:      Optional[int]	,      A	:      Optional[Any]	,      A	:      List[str]	,      A	:      int	,      A	:      str	,      A	:      List[Any]	,      A	:      Any	,      ):
           _UpperCAmelCase     :      Any														=       FlaubertWithLMHeadModel(_A		)
           model.to(_A		)
           model.eval()

           _UpperCAmelCase     :      List[str]														=       model(_A	,      token_type_ids=_A	,      labels=_A		)
           self.parent.assertEqual(result.loss.shape	,      ()		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)		)



      def 		snake_case_   (	self	:      Tuple	,      A	:      List[str]	,      A	:      List[str]	,      A	:      Optional[Any]	,      A	:      Union[str, Any]	,      A	:      str	,      A	:      List[str]	,      A	:      Tuple	,      A	:      Optional[int]	,      A	:      Dict	,      ):
           _UpperCAmelCase     :      Optional[Any]														=       FlaubertForQuestionAnsweringSimple(_A		)
           model.to(_A		)
           model.eval()

           _UpperCAmelCase     :      List[str]														=       model(_A		)

           _UpperCAmelCase     :      str														=       model(_A	,      start_positions=_A	,      end_positions=_A		)
           self.parent.assertEqual(result.start_logits.shape	,      (self.batch_size, self.seq_length)		)
           self.parent.assertEqual(result.end_logits.shape	,      (self.batch_size, self.seq_length)		)



      def 		snake_case_   (	self	:      Tuple	,      A	:      str	,      A	:      Tuple	,      A	:      Tuple	,      A	:      Union[str, Any]	,      A	:      List[str]	,      A	:      int	,      A	:      str	,      A	:      Dict	,      A	:      List[Any]	,      ):
           _UpperCAmelCase     :      Any														=       FlaubertForQuestionAnswering(_A		)
           model.to(_A		)
           model.eval()

           _UpperCAmelCase     :      List[str]														=       model(_A		)

           _UpperCAmelCase     :      Union[str, Any]														=       model(
               _A	,      start_positions=_A	,      end_positions=_A	,      cls_index=_A	,      is_impossible=_A	,      p_mask=_A	,      )

           _UpperCAmelCase     :      Optional[int]														=       model(
               _A	,      start_positions=_A	,      end_positions=_A	,      cls_index=_A	,      is_impossible=_A	,      )

           ((_UpperCAmelCase    )							,			    )     :      Any														=       result_with_labels.to_tuple()

           _UpperCAmelCase     :      Any														=       model(_A	,      start_positions=_A	,      end_positions=_A		)

           ((_UpperCAmelCase    )							,			    )     :      Union[str, Any]														=       result_with_labels.to_tuple()

           self.parent.assertEqual(result_with_labels.loss.shape	,      ()		)
           self.parent.assertEqual(result.start_top_log_probs.shape	,      (self.batch_size, model.config.start_n_top)		)
           self.parent.assertEqual(result.start_top_index.shape	,      (self.batch_size, model.config.start_n_top)		)
           self.parent.assertEqual(
               result.end_top_log_probs.shape	,      (self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
           self.parent.assertEqual(
               result.end_top_index.shape	,      (self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
           self.parent.assertEqual(result.cls_logits.shape	,      (self.batch_size,)		)



      def 		snake_case_   (	self	:      List[Any]	,      A	:      Union[str, Any]	,      A	:      Tuple	,      A	:      str	,      A	:      int	,      A	:      int	,      A	:      Optional[int]	,      A	:      Optional[int]	,      A	:      int	,      A	:      List[str]	,      ):
           _UpperCAmelCase     :      str														=       FlaubertForSequenceClassification(_A		)
           model.to(_A		)
           model.eval()

           _UpperCAmelCase     :      Tuple														=       model(_A		)
           _UpperCAmelCase     :      Dict														=       model(_A	,      labels=_A		)

           self.parent.assertEqual(result.loss.shape	,      ()		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      List[str]	,      A	:      Optional[Any]	,      A	:      str	,      A	:      Union[str, Any]	,      A	:      List[Any]	,      A	:      int	,      A	:      List[Any]	,      A	:      str	,      A	:      List[str]	,      ):
           _UpperCAmelCase     :      Optional[int]														=       self.num_labels
           _UpperCAmelCase     :      Union[str, Any]														=       FlaubertForTokenClassification(_A		)
           model.to(_A		)
           model.eval()

           _UpperCAmelCase     :      int														=       model(_A	,      attention_mask=_A	,      labels=_A		)
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)		)



      def 		snake_case_   (	self	:      Tuple	,      A	:      Dict	,      A	:      str	,      A	:      Optional[Any]	,      A	:      List[str]	,      A	:      Any	,      A	:      Optional[int]	,      A	:      Optional[Any]	,      A	:      List[Any]	,      A	:      List[str]	,      ):
           _UpperCAmelCase     :      str														=       self.num_choices
           _UpperCAmelCase     :      Optional[Any]														=       FlaubertForMultipleChoice(config=_A		)
           model.to(_A		)
           model.eval()
           _UpperCAmelCase     :      int														=       input_ids.unsqueeze(1		).expand(-1	,      self.num_choices	,      -1		).contiguous()
           _UpperCAmelCase     :      Optional[Any]														=       token_type_ids.unsqueeze(1		).expand(-1	,      self.num_choices	,      -1		).contiguous()
           _UpperCAmelCase     :      str														=       input_mask.unsqueeze(1		).expand(-1	,      self.num_choices	,      -1		).contiguous()
           _UpperCAmelCase     :      str														=       model(
               _A	,      attention_mask=_A	,      token_type_ids=_A	,      labels=_A	,      )
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_choices)		)



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.prepare_config_and_inputs()
           (
               (
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			(
               _UpperCAmelCase    
           )							,			    
           )     :      int														=       config_and_inputs
           _UpperCAmelCase     :      List[Any]														=       {
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
               "lengths": input_lengths,
               "attention_mask": input_mask,
           }
           return config, inputs_dict







@require_torch
class       UpperCAmelCase_    (							__lowercase			,     __lowercase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  List[str]						=					(
          (
              FlaubertModel,
              FlaubertWithLMHeadModel,
              FlaubertForQuestionAnswering,
              FlaubertForQuestionAnsweringSimple,
              FlaubertForSequenceClassification,
              FlaubertForTokenClassification,
              FlaubertForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:  Tuple						=					(
          {
              'feature-extraction': FlaubertModel,
              'fill-mask': FlaubertWithLMHeadModel,
              'question-answering': FlaubertForQuestionAnsweringSimple,
              'text-classification': FlaubertForSequenceClassification,
              'token-classification': FlaubertForTokenClassification,
              'zero-shot': FlaubertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Dict	,      A	:      Dict	,      A	:      Tuple	,      A	:      int	,      A	:      Any		):
           if (
               pipeline_test_casse_name == "QAPipelineTests"
               and tokenizer_name is not None
               and not tokenizer_name.endswith("Fast"		)
           ):
                # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                return True

           return False



      def 		snake_case_   (	self	:      str	,      A	:      Any	,      A	:      List[str]	,      A	:      Optional[int]=False		):
           _UpperCAmelCase     :      Optional[Any]														=       super()._prepare_for_class(_A	,      _A	,      return_labels=_A		)

           if return_labels:
                if model_class.__name__ == "FlaubertForQuestionAnswering":
                     _UpperCAmelCase     :      Tuple														=       torch.zeros(
                         self.model_tester.batch_size	,      dtype=torch.long	,      device=_A		)
                     _UpperCAmelCase     :      Optional[int]														=       torch.zeros(
                         self.model_tester.batch_size	,      dtype=torch.long	,      device=_A		)

           return inputs_dict



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      str														=       FlaubertModelTester(self		)
           _UpperCAmelCase     :      Optional[Any]														=       ConfigTester(self	,      config_class=_A	,      emb_dim=3_7		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           self.config_tester.run_common_tests()



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_model(*_A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_lm_head(*_A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_simple_qa(*_A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_qa(*_A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_sequence_classif(*_A		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Any														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_token_classif(*_A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_flaubert_multiple_choice(*_A		)



      @slow
      def 		snake_case_   (	self	:      str		):
           for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      Tuple														=       FlaubertModel.from_pretrained(_A		)
                self.assertIsNotNone(_A		)



      @slow
      @require_torch_gpu
      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[int]														=       self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
                if model_class == FlaubertForMultipleChoice:
                     return

                _UpperCAmelCase     :      List[str]														=       True
                _UpperCAmelCase     :      Dict														=       model_class(config=_A		)

                _UpperCAmelCase     :      List[Any]														=       self._prepare_for_class(_A	,      _A		)
                _UpperCAmelCase     :      Optional[int]														=       torch.jit.trace(
                    _A	,      (inputs_dict["input_ids"].to("cpu"		), inputs_dict["attention_mask"].to("cpu"		))		)

                with tempfile.TemporaryDirectory() as tmp:
                     torch.jit.save(_A	,      os.path.join(_A	,      "traced_model.pt"		)		)
                     _UpperCAmelCase     :      Union[str, Any]														=       torch.jit.load(os.path.join(_A	,      "traced_model.pt"		)	,      map_location=_A		)
                     loaded(inputs_dict["input_ids"].to(_A		)	,      inputs_dict["attention_mask"].to(_A		)		)







@require_torch
class       UpperCAmelCase_    (							unittest.TestCase		):



      @slow
      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      List[Any]														=       FlaubertModel.from_pretrained("flaubert/flaubert_base_cased"		)
           _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]		)
           with torch.no_grad():
                _UpperCAmelCase     :      Union[str, Any]														=       model(_A		)[0]
           _UpperCAmelCase     :      List[str]														=       torch.Size((1, 1_1, 7_6_8)		)
           self.assertEqual(output.shape	,      _A		)
           _UpperCAmelCase     :      List[Any]														=       torch.tensor(
               [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]		)

           self.assertTrue(torch.allclose(output[:, :3, :3]	,      _A	,      atol=1e-4		)		)

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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
])
_lowerCAmelCase       =		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

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

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

										if mod.modelId.startswith("CompVis"):
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
										else:
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint)

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

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		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!!!')
