






"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
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 import (
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertModel,
						)





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				int							    =   (
					    (
					        TFMobileBertModel,
					        TFMobileBertForMaskedLM,
					        TFMobileBertForNextSentencePrediction,
					        TFMobileBertForPreTraining,
					        TFMobileBertForQuestionAnswering,
					        TFMobileBertForSequenceClassification,
					        TFMobileBertForTokenClassification,
					        TFMobileBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				Any							    =   (
					    {
					        "feature-extraction": TFMobileBertModel,
					        "fill-mask": TFMobileBertForMaskedLM,
					        "question-answering": TFMobileBertForQuestionAnswering,
					        "text-classification": TFMobileBertForSequenceClassification,
					        "token-classification": TFMobileBertForTokenClassification,
					        "zero-shot": TFMobileBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:				str							    =   False
					SCREAMING_SNAKE_CASE_		:				int							    =   False




					def   A  (				self     :						Dict    ,						A     :						Dict    ,						A     :						List[Any]    ,						A     :						Tuple=False   )  ->  int:
							lowercase_ : str							       =   super()._prepare_for_class(A    ,						A    ,						return_labels=A   )

							if return_labels:
									if model_class in get_values(A   ):
											lowercase_ : List[str]							       =   tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa   )

							return inputs_dict





					class 					_UpperCAmelCase       ( _A							):




										def __init__(				self     :						Any    ,						A     :						int    ,						A     :						int=13    ,						A     :						Any=7    ,						A     :						Optional[Any]=True    ,						A     :						List[str]=True    ,						A     :						List[Any]=True    ,						A     :						Dict=True    ,						A     :						List[str]=99    ,						A     :						str=32    ,						A     :						str=32    ,						A     :						List[Any]=2    ,						A     :						Tuple=4    ,						A     :						Optional[Any]=37    ,						A     :						Tuple="gelu"    ,						A     :						Optional[int]=0.1    ,						A     :						Tuple=0.1    ,						A     :						Optional[int]=5_12    ,						A     :						List[Any]=16    ,						A     :						Optional[Any]=2    ,						A     :						Any=0.02    ,						A     :						List[str]=3    ,						A     :						Dict=4    ,						A     :						Tuple=None    ,						)  ->  List[Any]:
												lowercase_ : Optional[int]							       =   parent
												lowercase_ : Optional[int]							       =   batch_size
												lowercase_ : Union[str, Any]							       =   seq_length
												lowercase_ : List[str]							       =   is_training
												lowercase_ : str							       =   use_input_mask
												lowercase_ : Tuple							       =   use_token_type_ids
												lowercase_ : Any							       =   use_labels
												lowercase_ : Optional[int]							       =   vocab_size
												lowercase_ : Tuple							       =   hidden_size
												lowercase_ : List[str]							       =   num_hidden_layers
												lowercase_ : Dict							       =   num_attention_heads
												lowercase_ : Any							       =   intermediate_size
												lowercase_ : Tuple							       =   hidden_act
												lowercase_ : Optional[Any]							       =   hidden_dropout_prob
												lowercase_ : Optional[Any]							       =   attention_probs_dropout_prob
												lowercase_ : str							       =   max_position_embeddings
												lowercase_ : str							       =   type_vocab_size
												lowercase_ : Tuple							       =   type_sequence_label_size
												lowercase_ : str							       =   initializer_range
												lowercase_ : str							       =   num_labels
												lowercase_ : Optional[Any]							       =   num_choices
												lowercase_ : Any							       =   scope
												lowercase_ : int							       =   embedding_size




										def   A  (				self     :						List[Any]   )  ->  Any:
												lowercase_ : List[Any]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

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

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

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

												lowercase_ : Optional[Any]							       =   MobileBertConfig(
												    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    ,						embedding_size=self.embedding_size    ,						)

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




										def   A  (				self     :						List[str]    ,						A     :						List[str]    ,						A     :						Optional[int]    ,						A     :						str    ,						A     :						Union[str, Any]    ,						A     :						List[str]    ,						A     :						Optional[Any]    ,						A     :						Union[str, Any]   )  ->  Optional[Any]:
												lowercase_ : Optional[int]							       =   TFMobileBertModel(config=A   )
												lowercase_ : List[Any]							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : Tuple							       =   model(A   )

												lowercase_ : Optional[int]							       =   [input_ids, input_mask]
												lowercase_ : int							       =   model(A   )

												lowercase_ : Optional[int]							       =   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   A  (				self     :						List[Any]    ,						A     :						int    ,						A     :						int    ,						A     :						int    ,						A     :						int    ,						A     :						Optional[int]    ,						A     :						List[str]    ,						A     :						Dict   )  ->  List[Any]:
												lowercase_ : List[Any]							       =   TFMobileBertForMaskedLM(config=A   )
												lowercase_ : Tuple							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : Optional[int]							       =   model(A   )
												self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )




										def   A  (				self     :						List[Any]    ,						A     :						Optional[Any]    ,						A     :						int    ,						A     :						Dict    ,						A     :						Union[str, Any]    ,						A     :						List[Any]    ,						A     :						List[str]    ,						A     :						List[Any]   )  ->  Union[str, Any]:
												lowercase_ : Optional[int]							       =   TFMobileBertForNextSentencePrediction(config=A   )
												lowercase_ : int							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : Optional[int]							       =   model(A   )
												self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, 2)   )




										def   A  (				self     :						Any    ,						A     :						List[str]    ,						A     :						Union[str, Any]    ,						A     :						Any    ,						A     :						Any    ,						A     :						Tuple    ,						A     :						Optional[Any]    ,						A     :						str   )  ->  List[str]:
												lowercase_ : Optional[Any]							       =   TFMobileBertForPreTraining(config=A   )
												lowercase_ : List[str]							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : List[Any]							       =   model(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   A  (				self     :						Dict    ,						A     :						Optional[int]    ,						A     :						List[Any]    ,						A     :						List[Any]    ,						A     :						Union[str, Any]    ,						A     :						Optional[Any]    ,						A     :						Any    ,						A     :						str   )  ->  str:
												lowercase_ : Tuple							       =   self.num_labels
												lowercase_ : Optional[Any]							       =   TFMobileBertForSequenceClassification(config=A   )
												lowercase_ : Optional[int]							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : Optional[int]							       =   model(A   )
												self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )




										def   A  (				self     :						List[str]    ,						A     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						int    ,						A     :						Optional[int]    ,						A     :						List[Any]    ,						A     :						Any    ,						A     :						Tuple   )  ->  str:
												lowercase_ : Tuple							       =   self.num_choices
												lowercase_ : List[str]							       =   TFMobileBertForMultipleChoice(config=A   )
												lowercase_ : Tuple							       =   tf.tile(tf.expand_dims(A    ,						1   )    ,						(1, self.num_choices, 1)   )
												lowercase_ : Dict							       =   tf.tile(tf.expand_dims(A    ,						1   )    ,						(1, self.num_choices, 1)   )
												lowercase_ : List[str]							       =   tf.tile(tf.expand_dims(A    ,						1   )    ,						(1, self.num_choices, 1)   )
												lowercase_ : Optional[int]							       =   {
												    '''input_ids''': multiple_choice_inputs_ids,
												    '''attention_mask''': multiple_choice_input_mask,
												    '''token_type_ids''': multiple_choice_token_type_ids,
												}
												lowercase_ : Union[str, Any]							       =   model(A   )
												self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_choices)   )




										def   A  (				self     :						Tuple    ,						A     :						Optional[int]    ,						A     :						Optional[Any]    ,						A     :						Any    ,						A     :						Dict    ,						A     :						Optional[int]    ,						A     :						Dict    ,						A     :						str   )  ->  str:
												lowercase_ : Optional[Any]							       =   self.num_labels
												lowercase_ : Optional[int]							       =   TFMobileBertForTokenClassification(config=A   )
												lowercase_ : List[Any]							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : Tuple							       =   model(A   )
												self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels)   )




										def   A  (				self     :						Dict    ,						A     :						Dict    ,						A     :						Optional[Any]    ,						A     :						List[Any]    ,						A     :						Union[str, Any]    ,						A     :						List[str]    ,						A     :						int    ,						A     :						str   )  ->  Optional[Any]:
												lowercase_ : Optional[int]							       =   TFMobileBertForQuestionAnswering(config=A   )
												lowercase_ : str							       =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
												lowercase_ : Union[str, Any]							       =   model(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   A  (				self     :						int   )  ->  Union[str, Any]:
												lowercase_ : Optional[int]							       =   self.prepare_config_and_inputs()
												(
												    (
												    lowercase_
												)       ,					(
												    lowercase_
												)       ,					(
												    lowercase_
												)       ,					(
												    lowercase_
												)       ,					(
												    lowercase_
												)       ,					(
												    lowercase_
												)       ,					(
												    lowercase_
												)       ,					
												) : Any							       =   config_and_inputs
												lowercase_ : List[str]							       =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict




					def   A  (				self     :						Tuple   )  ->  Tuple:
							lowercase_ : List[Any]							       =   TFMobileBertModelTest.TFMobileBertModelTester(self   )
							lowercase_ : List[str]							       =   ConfigTester(self    ,						config_class=A    ,						hidden_size=37   )




					def   A  (				self     :						int   )  ->  Optional[Any]:
							self.config_tester.run_common_tests()




					def   A  (				self     :						List[str]   )  ->  Tuple:
							lowercase_ : Any							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_model(*A   )




					def   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_masked_lm(*A   )




					def   A  (				self     :						List[str]   )  ->  List[Any]:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_multiple_choice(*A   )




					def   A  (				self     :						str   )  ->  Any:
							lowercase_ : Union[str, Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*A   )




					def   A  (				self     :						Optional[int]   )  ->  int:
							lowercase_ : List[str]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_pretraining(*A   )




					def   A  (				self     :						str   )  ->  int:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_question_answering(*A   )




					def   A  (				self     :						Optional[Any]   )  ->  List[str]:
							lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_sequence_classification(*A   )




					def   A  (				self     :						str   )  ->  Any:
							lowercase_ : Union[str, Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mobilebert_for_token_classification(*A   )






					@slow
					def   A  (				self     :						Optional[Any]   )  ->  Optional[Any]:
							# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["google/mobilebert-uncased"]:
									lowercase_ : Any							       =   TFMobileBertModel.from_pretrained(A   )
									self.assertIsNotNone(A   )





@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : Any							       =   TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''   )
							lowercase_ : Any							       =   tf.constant([[0, 1, 2, 3, 4, 5]]   )
							lowercase_ : List[Any]							       =   model(A   )[0]

							lowercase_ : Optional[int]							       =   [1, 6, 3_05_22]
							self.assertEqual(output.shape    ,						A   )

							lowercase_ : Tuple							       =   tf.constant(
							    [
							        [
							            [-4.5919547, -9.248295, -9.645256],
							            [-6.7306175, -6.440284, -6.6052837],
							            [-7.2743506, -6.7847915, -6.024673],
							        ]
							    ]   )
							tf.debugging.assert_near(output[:, :3, :3]    ,						A    ,						atol=1e-4   )











"""simple docstring"""



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						__A		:       str  				=		argparse.ArgumentParser()

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						parser.add_argument(
						    '''--txt2img_unclip''',
						    default='''kakaobrain/karlo-v1-alpha''',
						    type=str,
						    required=False,
						    help='''The pretrained txt2img unclip.''',
						)

						__A		:       str  				=		parser.parse_args()

						__A		:       List[Any]  				=		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						__A		:       Dict  				=		CLIPImageProcessor()
						__A		:       Union[str, Any]  				=		CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

						__A		:       List[str]  				=		UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)



