






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:Any        =      parent
								__magic_name__		:Tuple        =      1_3
								__magic_name__		:Any        =      7
								__magic_name__		:str        =      True
								__magic_name__		:Dict        =      True
								__magic_name__		:List[Any]        =      False
								__magic_name__		:int        =      True
								__magic_name__		:str        =      9_9
								__magic_name__		:Optional[int]        =      3_2
								__magic_name__		:Any        =      2
								__magic_name__		:Optional[int]        =      4
								__magic_name__		:int        =      3_7
								__magic_name__		:Tuple        =      '''gelu'''
								__magic_name__		:List[str]        =      0.1
								__magic_name__		:Optional[int]        =      0.1
								__magic_name__		:int        =      5_1_2
								__magic_name__		:List[str]        =      1_6
								__magic_name__		:Any        =      2
								__magic_name__		:List[Any]        =      0.02
								__magic_name__		:str        =      3
								__magic_name__		:List[Any]        =      4
								__magic_name__		:List[Any]        =      None







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size     )

								__magic_name__		:Optional[Any]        =      None
								if self.use_input_mask:
												__magic_name__		:List[Any]        =      random_attention_mask([self.batch_size, self.seq_length]     )

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

								__magic_name__		:Union[str, Any]        =      DistilBertConfig(
								    vocab_size=self.vocab_size       ,					dim=self.hidden_size       ,					n_layers=self.num_hidden_layers       ,					n_heads=self.num_attention_heads       ,					hidden_dim=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					dropout=self.hidden_dropout_prob       ,					attention_dropout=self.attention_probs_dropout_prob       ,					max_position_embeddings=self.max_position_embeddings       ,					initializer_range=self.initializer_range       ,					)

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      TFDistilBertModel(config=__lowerCAmelCase     )
								__magic_name__		:int        =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}

								__magic_name__		:str        =      model(__lowerCAmelCase     )

								__magic_name__		:str        =      [input_ids, input_mask]

								__magic_name__		:List[Any]        =      model(__lowerCAmelCase     )

								self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size)     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:str        =      TFDistilBertForMaskedLM(config=__lowerCAmelCase     )
								__magic_name__		:Dict        =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__magic_name__		:Tuple        =      model(__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.vocab_size)     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:str        =      TFDistilBertForQuestionAnswering(config=__lowerCAmelCase     )
								__magic_name__		:Optional[int]        =      {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								}
								__magic_name__		:List[str]        =      model(__lowerCAmelCase     )
								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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.num_labels
								__magic_name__		:Any        =      TFDistilBertForSequenceClassification(__lowerCAmelCase     )
								__magic_name__		:int        =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__magic_name__		:List[str]        =      model(__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.num_choices
								__magic_name__		:Optional[int]        =      TFDistilBertForMultipleChoice(__lowerCAmelCase     )
								__magic_name__		:Any        =      tf.tile(tf.expand_dims(__lowerCAmelCase       ,					1     )       ,					(1, self.num_choices, 1)     )
								__magic_name__		:Optional[int]        =      tf.tile(tf.expand_dims(__lowerCAmelCase       ,					1     )       ,					(1, self.num_choices, 1)     )
								__magic_name__		:List[Any]        =      {
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								}
								__magic_name__		:int        =      model(__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices)     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.num_labels
								__magic_name__		:Union[str, Any]        =      TFDistilBertForTokenClassification(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__magic_name__		:str        =      model(__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels)     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.prepare_config_and_inputs()
								((__magic_name__)     ,				(__magic_name__)     ,				(__magic_name__)     ,				(__magic_name__)     ,				(__magic_name__)     ,				(__magic_name__))		:Any        =      config_and_inputs
								__magic_name__		:Optional[int]        =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict







@require_tf
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (
				    (
				        TFDistilBertModel,
				        TFDistilBertForMaskedLM,
				        TFDistilBertForQuestionAnswering,
				        TFDistilBertForSequenceClassification,
				        TFDistilBertForTokenClassification,
				        TFDistilBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else None
				)
				a__					       =  (
				    {
				        '''feature-extraction''': TFDistilBertModel,
				        '''fill-mask''': TFDistilBertForMaskedLM,
				        '''question-answering''': TFDistilBertForQuestionAnswering,
				        '''text-classification''': TFDistilBertForSequenceClassification,
				        '''token-classification''': TFDistilBertForTokenClassification,
				        '''zero-shot''': TFDistilBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      TFDistilBertModelTester(self     )
								__magic_name__		:Any        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					dim=3_7     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.config_tester.run_common_tests()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_masked_lm(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_question_answering(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_sequence_classification(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_multiple_choice(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_token_classification(*__lowerCAmelCase     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]     ):
												__magic_name__		:Any        =      TFDistilBertModel.from_pretrained(__lowerCAmelCase     )
												self.assertIsNotNone(__lowerCAmelCase     )







@require_tf
class 						lowerCamelCase_				(      unittest.TestCase ):







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''     )
								__magic_name__		:str        =      tf.constant([[0, 1, 2, 3, 4, 5]]     )
								__magic_name__		:List[str]        =      model(__lowerCAmelCase     )[0]

								__magic_name__		:Union[str, Any]        =      [1, 6, 7_6_8]
								self.assertEqual(output.shape       ,					__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      tf.constant(
								    [
								        [
								            [0.19261885, -0.13732955, 0.4119799],
								            [0.22150156, -0.07422661, 0.39037204],
								            [0.22756018, -0.0896414, 0.3701467],
								        ]
								    ]     )
								tf.debugging.assert_near(output[:, :3, :3]       ,					__lowerCAmelCase       ,					atol=1E-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      __lowercase					( ):







				"""simple docstring"""






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

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

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

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      torch.Generator(pipeline.device ).manual_seed(snake_case )
				__magic_name__		:str        =      pipeline(
				    snake_case,  guidance_scale=snake_case,  num_inference_steps=snake_case,  generator=snake_case,  num_images_per_prompt=snake_case,  ).images
				__magic_name__		:Tuple        =      int(math.sqrt(snake_case ) )
				__magic_name__		:Union[str, Any]        =      image_grid(snake_case,  rows=_rows,  cols=num_images_per_prompt // _rows )
				return grid, images


SCREAMING_SNAKE_CASE__      :			Any							=		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__      :			int							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE__      :			str							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE__      :			int							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__      :			str							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					SCREAMING_SNAKE_CASE__      :			str							=		load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					SCREAMING_SNAKE_CASE__      :			Dict							=		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE__      :			Any							=		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		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()))))
SCREAMING_SNAKE_CASE__      :			int							=		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)))






