




'''simple docstring'''



import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
          NezhaModel,
      )
      from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A,							A=13,							A=7,							A=True,							A=True,							A=True,							A=True,							A=99,							A=32,							A=5,							A=4,							A=37,							A="gelu",							A=0.1,							A=0.1,							A=128,							A=32,							A=16,							A=2,							A=0.02,							A=3,							A=4,							A=None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						parent
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						batch_size
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						seq_length
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						is_training
             SCREAMING_SNAKE_CASE   :      Any    					=						use_input_mask
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						use_token_type_ids
             SCREAMING_SNAKE_CASE   :      int    					=						use_labels
             SCREAMING_SNAKE_CASE   :      Tuple    					=						vocab_size
             SCREAMING_SNAKE_CASE   :      Dict    					=						hidden_size
             SCREAMING_SNAKE_CASE   :      List[str]    					=						num_hidden_layers
             SCREAMING_SNAKE_CASE   :      Any    					=						num_attention_heads
             SCREAMING_SNAKE_CASE   :      Any    					=						intermediate_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						hidden_act
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						hidden_dropout_prob
             SCREAMING_SNAKE_CASE   :      Tuple    					=						attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE   :      Any    					=						max_position_embeddings
             SCREAMING_SNAKE_CASE   :      Dict    					=						type_vocab_size
             SCREAMING_SNAKE_CASE   :      int    					=						type_sequence_label_size
             SCREAMING_SNAKE_CASE   :      Any    					=						initializer_range
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						num_labels
             SCREAMING_SNAKE_CASE   :      str    					=						num_choices
             SCREAMING_SNAKE_CASE   :      str    					=						scope







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						ids_tensor([self.batch_size, self.seq_length],							self.vocab_size  )

             SCREAMING_SNAKE_CASE   :      int    					=						None
             if self.use_input_mask:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						random_attention_mask([self.batch_size, self.seq_length]  )

             SCREAMING_SNAKE_CASE   :      int    					=						None
             if self.use_token_type_ids:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						ids_tensor([self.batch_size, self.seq_length],							self.type_vocab_size  )

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

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return NezhaConfig(
                 vocab_size=self.vocab_size,							hidden_size=self.hidden_size,							num_hidden_layers=self.num_hidden_layers,							num_attention_heads=self.num_attention_heads,							intermediate_size=self.intermediate_size,							hidden_act=self.hidden_act,							hidden_dropout_prob=self.hidden_dropout_prob,							attention_probs_dropout_prob=self.attention_probs_dropout_prob,							max_position_embeddings=self.max_position_embeddings,							type_vocab_size=self.type_vocab_size,							is_decoder=A,							initializer_range=self.initializer_range,							)







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             (
                 (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    
             )   :      Union[str, Any]    					=						self.prepare_config_and_inputs()

             SCREAMING_SNAKE_CASE   :      List[str]    					=						True
             SCREAMING_SNAKE_CASE   :      Tuple    					=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
             SCREAMING_SNAKE_CASE   :      Any    					=						ids_tensor([self.batch_size, self.seq_length],							vocab_size=2  )

             return (
                 config,
                 input_ids,
                 token_type_ids,
                 input_mask,
                 sequence_labels,
                 token_labels,
                 choice_labels,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						NezhaModel(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(A,							attention_mask=A,							token_type_ids=A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(A,							token_type_ids=A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						model(A  )
             self.parent.assertEqual(result.last_hidden_state.shape,							(self.batch_size, self.seq_length, self.hidden_size)  )
             self.parent.assertEqual(result.pooler_output.shape,							(self.batch_size, self.hidden_size)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A,							A,							A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						True
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						NezhaModel(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      str    					=						model(
                 A,							attention_mask=A,							token_type_ids=A,							encoder_hidden_states=A,							encoder_attention_mask=A,							)
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model(
                 A,							attention_mask=A,							token_type_ids=A,							encoder_hidden_states=A,							)
             SCREAMING_SNAKE_CASE   :      Tuple    					=						model(A,							attention_mask=A,							token_type_ids=A  )
             self.parent.assertEqual(result.last_hidden_state.shape,							(self.batch_size, self.seq_length, self.hidden_size)  )
             self.parent.assertEqual(result.pooler_output.shape,							(self.batch_size, self.hidden_size)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						NezhaForMaskedLM(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(A,							attention_mask=A,							token_type_ids=A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.seq_length, self.vocab_size)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						NezhaForNextSentencePrediction(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Dict    					=						model(
                 A,							attention_mask=A,							token_type_ids=A,							labels=A,							)
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, 2)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						NezhaForPreTraining(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      int    					=						model(
                 A,							attention_mask=A,							token_type_ids=A,							labels=A,							next_sentence_label=A,							)
             self.parent.assertEqual(result.prediction_logits.shape,							(self.batch_size, self.seq_length, self.vocab_size)  )
             self.parent.assertEqual(result.seq_relationship_logits.shape,							(self.batch_size, 2)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						NezhaForQuestionAnswering(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model(
                 A,							attention_mask=A,							token_type_ids=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        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      Dict    					=						NezhaForSequenceClassification(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model(A,							attention_mask=A,							token_type_ids=A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.num_labels)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						NezhaForTokenClassification(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      int    					=						model(A,							attention_mask=A,							token_type_ids=A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.seq_length, self.num_labels)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A,							A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.prepare_config_and_inputs()
             (
                 (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    
             )   :      List[Any]    					=						config_and_inputs
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict






@require_torch
class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				str        =						(
           (
               NezhaModel,
               NezhaForMaskedLM,
               NezhaForMultipleChoice,
               NezhaForNextSentencePrediction,
               NezhaForPreTraining,
               NezhaForQuestionAnswering,
               NezhaForSequenceClassification,
               NezhaForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       A						:				List[str]        =						(
           {
               '''feature-extraction''': NezhaModel,
               '''fill-mask''': NezhaForMaskedLM,
               '''question-answering''': NezhaForQuestionAnswering,
               '''text-classification''': NezhaForSequenceClassification,
               '''token-classification''': NezhaForTokenClassification,
               '''zero-shot''': NezhaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       A						:				List[str]        =						True







       def        UpperCamelCase_							(      self,							A,							A,							A=False  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						super()._prepare_for_class(A,							A,							return_labels=A  )

             if return_labels:
                   if model_class in get_values(A  ):
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.seq_length),							dtype=torch.long,							device=A  )
                         SCREAMING_SNAKE_CASE   :      str    					=						torch.zeros(
                             self.model_tester.batch_size,							dtype=torch.long,							device=A  )
             return inputs_dict







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						NezhaModelTester(self  )
             SCREAMING_SNAKE_CASE   :      Any    					=						ConfigTester(self,							config_class=A,							hidden_size=37  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.config_tester.run_common_tests()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             (
                 (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    
             )   :      List[str]    					=						self.model_tester.prepare_config_and_inputs_for_decoder()

             SCREAMING_SNAKE_CASE   :      int    					=						None

             self.model_tester.create_and_check_model_as_decoder(
                 A,							A,							A,							A,							A,							A,							A,							A,							A,							)







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_next_sequence_prediction(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						NezhaModel.from_pretrained(A  )
                   self.assertIsNotNone(A  )







       @slow
       @require_torch_gpu
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                   # NezhaForMultipleChoice behaves incorrectly in JIT environments.
                   if model_class == NezhaForMultipleChoice:
                         return

                   SCREAMING_SNAKE_CASE   :      int    					=						True
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model_class(config=A  )

                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self._prepare_for_class(A,							A  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						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,							'bert.pt'  )  )
                         SCREAMING_SNAKE_CASE   :      int    					=						torch.jit.load(os.path.join(A,							'bert.pt'  ),							map_location=A  )
                         loaded(inputs_dict['input_ids'].to(A  ),							inputs_dict['attention_mask'].to(A  )  )






@require_torch
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						NezhaModel.from_pretrained('sijunhe/nezha-cn-base'  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.tensor([[0, 1, 2, 3, 4, 5]]  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						torch.tensor([[0, 1, 1, 1, 1, 1]]  )
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Any    					=						model(A,							attention_mask=A  )[0]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Size((1, 6, 768)  )
             self.assertEqual(output.shape,							A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]]  )

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4],							A,							atol=1E-4  )  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.tensor([[0, 1, 2, 3, 4, 5]]  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.tensor([[1, 1, 1, 1, 1, 1]]  )
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model(A,							attention_mask=A  )[0]
             SCREAMING_SNAKE_CASE   :      Any    					=						torch.Size((1, 6, 21_128)  )
             self.assertEqual(output.shape,							A  )
             SCREAMING_SNAKE_CASE   :      str    					=						torch.tensor(
                 [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]]  )

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4],							A,							atol=1E-4  )  )








'''simple docstring'''



import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A = "cpu",							A = "openai/clip-vit-large-patch14"  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						device
             SCREAMING_SNAKE_CASE   :      Tuple    					=						CLIPTokenizerFast.from_pretrained(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
             SCREAMING_SNAKE_CASE   :      str    					=						[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
             SCREAMING_SNAKE_CASE   :      Dict    					=						torchvision.transforms.Normalize(self.image_mean,							self.image_std  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						torchvision.transforms.Resize(224  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torchvision.transforms.CenterCrop(224  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.resize(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.center_crop(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.normalize(A  )
             return images







       def __call__(      self,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.tokenizer(text=A,							**A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.preprocess_img(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						{key: value.to(self.device  ) for (key, value) in encoding.items()}
             return encoding






class 							_a				(			nn.Module ):





       '''simple docstring'''






       def __init__(      self,							A=10,							A=0.01,							A=None,							A=None,							A=None,							A=None,							A=None,							A=None,							A=False,							A=True,							A="image",							A=True,							A=False,							A=False,							A=False,							):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						None
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						device if device else get_device()
             if vqgan:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						vqgan
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						load_vqgan(self.device,							conf_path=A,							ckpt_path=A  )
             self.vqgan.eval()
             if clip:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						clip
             else:
                   SCREAMING_SNAKE_CASE   :      Any    					=						CLIPModel.from_pretrained('openai/clip-vit-base-patch32'  )
             self.clip.to(self.device  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ProcessorGradientFlow(device=self.device  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						iterations
             SCREAMING_SNAKE_CASE   :      Tuple    					=						lr
             SCREAMING_SNAKE_CASE   :      Tuple    					=						log
             SCREAMING_SNAKE_CASE   :      str    					=						make_grid
             SCREAMING_SNAKE_CASE   :      Dict    					=						return_val
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						quantize
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.decoder.z_shape







       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=5,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[]
             if output_path is None:
                   SCREAMING_SNAKE_CASE   :      int    					=						'./animation.gif'
             if input_path is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.save_path
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						sorted(glob(input_path + '/*'  )  )
             if not len(A  ):
                   raise ValueError(
                       'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                       ' function?)'  )
             if len(A  ) == 1:
                   print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						total_duration / len(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						[frame_duration] * len(A  )
             if extend_frames:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						1.5
                   SCREAMING_SNAKE_CASE   :      int    					=						3
             for file_name in paths:
                   if file_name.endswith('.png'  ):
                         images.append(imageio.imread(A  )  )
             imageio.mimsave(A,							A,							duration=A  )
             print(F"gif saved to {output_path}"  )







       def        UpperCamelCase_							(      self,							A=None,							A=None  ):







             '''simple docstring'''







             if not (path or img):
                   raise ValueError('Input either path or tensor'  )
             if img is not None:
                   raise NotImplementedError
             SCREAMING_SNAKE_CASE   :      str    					=						preprocess(Image.open(A  ),							target_image_size=256  ).to(self.device  )
             SCREAMING_SNAKE_CASE   :      Any    					=						preprocess_vqgan(A  )
             SCREAMING_SNAKE_CASE					,    *SCREAMING_SNAKE_CASE   :      Tuple    					=						self.vqgan.encode(A  )
             return z







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.latent.detach().requires_grad_()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						base_latent + transform_vector
             if self.quantize:
                   SCREAMING_SNAKE_CASE					,    *SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.quantize(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						trans_latent
             return self.vqgan.decode(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A=None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.clip_preprocessor(text=A,							images=A,							return_tensors='pt',							padding=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						self.clip(**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						clip_outputs.logits_per_image
             if weights is not None:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						similarity_logits * weights
             return similarity_logits.sum()







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._get_clip_similarity(pos_prompts['prompts'],							A,							weights=(1 / pos_prompts['weights'])  )
             if neg_prompts:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self._get_clip_similarity(neg_prompts['prompts'],							A,							weights=neg_prompts['weights']  )
             else:
                   SCREAMING_SNAKE_CASE   :      str    					=						torch.tensor([1],							device=self.device  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						-torch.log(A  ) + torch.log(A  )
             return loss







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.randn_like(self.latent,							requires_grad=A,							device=self.device  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.optim.Adam([vector],							lr=self.lr  )

             for i in range(self.iterations  ):
                   optim.zero_grad()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._add_vector(A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						loop_post_process(A  )
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self._get_CLIP_loss(A,							A,							A  )
                   print('CLIP loss',							A  )
                   if self.log:
                         wandb.log({'CLIP Loss': clip_loss}  )
                   clip_loss.backward(retain_graph=A  )
                   optim.step()
                   if self.return_val == "image":
                         yield custom_to_pil(transformed_img[0]  )
                   else:
                         yield vector







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             wandb.init(reinit=A,							project='face-editor'  )
             wandb.config.update({'Positive Prompts': positive_prompts}  )
             wandb.config.update({'Negative Prompts': negative_prompts}  )
             wandb.config.update({'lr': self.lr, 'iterations': self.iterations}  )
             if image_path:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						Image.open(A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						image.resize((256, 256)  )
                   wandb.log('Original Image',							wandb.Image(A  )  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if not prompts:
                   return []
             SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[]
             if isinstance(A,							A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[prompt.strip() for prompt in prompts.split('|'  )]
             for prompt in prompts:
                   if isinstance(A,							(tuple, list)  ):
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						prompt[0]
                         SCREAMING_SNAKE_CASE   :      Any    					=						float(prompt[1]  )
                   elif ":" in prompt:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						prompt.split(':'  )
                         SCREAMING_SNAKE_CASE   :      Any    					=						float(A  )
                   else:
                         SCREAMING_SNAKE_CASE   :      Dict    					=						prompt
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						1.0
                   processed_prompts.append(A  )
                   weights.append(A  )
             return {
                 "prompts": processed_prompts,
                 "weights": torch.tensor(A,							device=self.device  ),
             }







       def        UpperCamelCase_							(      self,							A,							A=None,							A=None,							A=True,							A=False,							A=True,							A=True,							A=None,							):







             '''simple docstring'''







             if image_path:
                   SCREAMING_SNAKE_CASE   :      int    					=						self._get_latent(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.randn(self.latent_dim,							device=self.device  )
             if self.log:
                   self._init_logging(A,							A,							A  )

             assert pos_prompts, "You must provide at least one positive prompt."
             SCREAMING_SNAKE_CASE   :      Dict    					=						self.process_prompts(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.process_prompts(A  )
             if save_final and save_path is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						os.path.join('./outputs/',							'_'.join(pos_prompts['prompts']  )  )
             if not os.path.exists(A  ):
                   os.makedirs(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						save_path + '_' + get_timestamp()
                   os.makedirs(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						save_path

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.vqgan.decode(self.latent  )[0]
             if show_intermediate:
                   print('Original Image'  )
                   show_pil(custom_to_pil(A  )  )

             SCREAMING_SNAKE_CASE   :      int    					=						loop_post_process(A  )
             for iter, transformed_img in enumerate(self._optimize_CLIP(A,							A,							A  )  ):
                   if show_intermediate:
                         show_pil(A  )
                   if save_intermediate:
                         transformed_img.save(os.path.join(self.save_path,							F"iter_{iter:03d}.png"  )  )
                   if self.log:
                         wandb.log({'Image': wandb.Image(A  )}  )
             if show_final:
                   show_pil(A  )
             if save_final:
                   transformed_img.save(os.path.join(self.save_path,							F"iter_{iter:03d}_final.png"  )  )


