import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
    )
    from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class     a_			(						_a						):






 '''simple docstring'''






 def __init__(      self :  Optional[int]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  List[str]=13      ,   lowercase__ :  Tuple=7      ,   lowercase__ :  List[Any]=True      ,   lowercase__ :  Dict=True      ,   lowercase__ :  int=True      ,   lowercase__ :  int=True      ,   lowercase__ :  List[Any]=99      ,   lowercase__ :  List[str]=32      ,   lowercase__ :  Optional[int]=5      ,   lowercase__ :  List[Any]=4      ,   lowercase__ :  Any=37      ,   lowercase__ :  Optional[int]="gelu"      ,   lowercase__ :  str=0.1      ,   lowercase__ :  Optional[Any]=0.1      ,   lowercase__ :  List[str]=512      ,   lowercase__ :  Union[str, Any]=16      ,   lowercase__ :  Dict=2      ,   lowercase__ :  int=0.02      ,   lowercase__ :  List[str]=False      ,   lowercase__ :  Optional[int]=True      ,   lowercase__ :  str="None"      ,   lowercase__ :  Dict=3      ,   lowercase__ :  Dict=4      ,   lowercase__ :  List[str]=None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = seq_length
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = use_input_mask
        lowerCAmelCase__					     = use_token_type_ids
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = intermediate_size
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = type_vocab_size
        lowerCAmelCase__					     = type_sequence_label_size
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = num_labels
        lowerCAmelCase__					     = num_choices
        lowerCAmelCase__					     = relative_attention
        lowerCAmelCase__					     = position_biased_input
        lowerCAmelCase__					     = pos_att_type
        lowerCAmelCase__					     = scope




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)

        lowerCAmelCase__					     = None
        if self.use_input_mask:
               lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   vocab_size=2)

        lowerCAmelCase__					     = None
        if self.use_token_type_ids:
               lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size)

        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = None
        if self.use_labels:
               lowerCAmelCase__					     = ids_tensor([self.batch_size]      ,   self.type_sequence_label_size)
               lowerCAmelCase__					     = ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels)
               lowerCAmelCase__					     = ids_tensor([self.batch_size]      ,   self.num_choices)

        lowerCAmelCase__					     = self.get_config()

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




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return DebertaConfig(
            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      ,   initializer_range=self.initializer_range      ,   relative_attention=self.relative_attention      ,   position_biased_input=self.position_biased_input      ,   pos_att_type=self.pos_att_type      ,   )




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_config()
        lowerCAmelCase__					     = 300
        return config




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        self.parent.assertListEqual(list(result.loss.size())      ,   [])




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  List[Any]      ,   lowercase__ :  int      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   lowercase__ :  Dict      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = DebertaModel(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   token_type_ids=lowercase__)[0]
        lowerCAmelCase__					     = model(lowercase__      ,   token_type_ids=lowercase__)[0]
        lowerCAmelCase__					     = model(lowercase__)[0]

        self.parent.assertListEqual(list(sequence_output.size())      ,   [self.batch_size, self.seq_length, self.hidden_size])




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  Dict      ,   lowercase__ :  Dict      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Dict      ,   lowercase__ :  Tuple      ,   lowercase__ :  str      ,   lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = DebertaForMaskedLM(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   token_type_ids=lowercase__      ,   labels=lowercase__)

        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size))




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  List[str]      ,   lowercase__ :  str      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  int      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_labels
        lowerCAmelCase__					     = DebertaForSequenceClassification(lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   token_type_ids=lowercase__      ,   labels=lowercase__)
        self.parent.assertListEqual(list(result.logits.size())      ,   [self.batch_size, self.num_labels])
        self.check_loss_output(lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Dict      ,   lowercase__ :  Tuple      ,   lowercase__ :  int      ,   lowercase__ :  Dict      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_labels
        lowerCAmelCase__					     = DebertaForTokenClassification(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__      ,   token_type_ids=lowercase__      ,   labels=lowercase__)
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels))




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  Tuple      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  Tuple      ,   lowercase__ :  List[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = DebertaForQuestionAnswering(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        lowerCAmelCase__					     = model(
            lowercase__      ,   attention_mask=lowercase__      ,   token_type_ids=lowercase__      ,   start_positions=lowercase__      ,   end_positions=lowercase__      ,   )
        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 :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.prepare_config_and_inputs()
        (
            lowerCAmelCase__    
        )					     = config_and_inputs
        lowerCAmelCase__					     = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict






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






 '''simple docstring'''






 UpperCAmelCase_     			= (
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase_     			= (
     {
         """feature-extraction""": DebertaModel,
         """fill-mask""": DebertaForMaskedLM,
         """question-answering""": DebertaForQuestionAnswering,
         """text-classification""": DebertaForSequenceClassification,
         """token-classification""": DebertaForTokenClassification,
         """zero-shot""": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = DebertaModelTester(self)
        lowerCAmelCase__					     = ConfigTester(self      ,   config_class=lowercase__      ,   hidden_size=37)




 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*lowercase__)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase__)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*lowercase__)




 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*lowercase__)




 @slow
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__					     = DebertaModel.from_pretrained(lowercase__)
               self.assertIsNotNone(lowercase__)






@require_torch
@require_sentencepiece
@require_tokenizers
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 @unittest.skip(reason='Model not available yet')
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        pass




 @slow
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = DebertaModel.from_pretrained('microsoft/deberta-base')

        lowerCAmelCase__					     = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
        lowerCAmelCase__					     = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
        with torch.no_grad():
               lowerCAmelCase__					     = model(lowercase__      ,   attention_mask=lowercase__)[0]
        # compare the actual values for a slice.
        lowerCAmelCase__					     = torch.tensor(
            [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]])
        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,   lowercase__      ,   atol=1e-4)      ,   F"""{output[:, 1:4, 1:4]}""")

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if not len(lowerCAmelCase__     ) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'     )

       lowerCAmelCase__     , lowerCAmelCase__					     = imgs[0].size
       lowerCAmelCase__					     = Image.new('RGB'     ,  size=(cols * w, rows * h)     )
       lowerCAmelCase__     , lowerCAmelCase__					     = grid.size

       for i, img in enumerate(lowerCAmelCase__     ):
              grid.paste(lowerCAmelCase__     ,  box=(i % cols * w, i // cols * h)     )
       return grid






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  rows=_rows     ,  cols=num_images_per_prompt // _rows     )
       return grid, images


lowerCAmelCase__     						=			parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__     						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__     						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__     						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__     						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase__     						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__     						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
    lowerCAmelCase__     						=			load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, 'unet', unet)
else:
    lowerCAmelCase__     						=			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__     						=			pipeline.to(unet.device)
lowerCAmelCase__						,		lowerCAmelCase__     						=			generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase__     						=			os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
    image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
