
"""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 	lowerCAmelCase_						(							_a							,							_a							,							unittest.TestCase      ):



							'''simple docstring'''





							_snake_case			=						(
							    (
							        TFMobileBertModel,
							        TFMobileBertForMaskedLM,
							        TFMobileBertForNextSentencePrediction,
							        TFMobileBertForPreTraining,
							        TFMobileBertForQuestionAnswering,
							        TFMobileBertForSequenceClassification,
							        TFMobileBertForTokenClassification,
							        TFMobileBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							_snake_case			=						(
							    {
							        '''feature-extraction''': TFMobileBertModel,
							        '''fill-mask''': TFMobileBertForMaskedLM,
							        '''question-answering''': TFMobileBertForQuestionAnswering,
							        '''text-classification''': TFMobileBertForSequenceClassification,
							        '''token-classification''': TFMobileBertForTokenClassification,
							        '''zero-shot''': TFMobileBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							_snake_case			=						False
							_snake_case			=						False




							def 					A__		(      self  ,							snake_case_  ,							snake_case_  ,							snake_case_=False							)    ->   int:
													__lowerCAmelCase   					=   super()._prepare_for_class(snake_case_  ,							snake_case_  ,							return_labels=snake_case_							)

													if return_labels:
																			if model_class in get_values(snake_case_							):
																									__lowerCAmelCase   					=   tf.zeros(self.model_tester.batch_size  ,							dtype=tf.intaa							)

													return inputs_dict
							class 	lowerCAmelCase_						(							_a      ):



														'''simple docstring'''





														def __init__(      self  ,							snake_case_  ,							snake_case_=13  ,							snake_case_=7  ,							snake_case_=True  ,							snake_case_=True  ,							snake_case_=True  ,							snake_case_=True  ,							snake_case_=99  ,							snake_case_=32  ,							snake_case_=32  ,							snake_case_=2  ,							snake_case_=4  ,							snake_case_=37  ,							snake_case_="gelu"  ,							snake_case_=0.1  ,							snake_case_=0.1  ,							snake_case_=512  ,							snake_case_=16  ,							snake_case_=2  ,							snake_case_=0.02  ,							snake_case_=3  ,							snake_case_=4  ,							snake_case_=None  ,							)    ->   int:
																				__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   					=   scope
																				__lowerCAmelCase   					=   embedding_size




														def 					A__		(      self							)    ->   Dict:
																				__lowerCAmelCase   					=   ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size							)

																				__lowerCAmelCase   					=   None
																				if self.use_input_mask:
																										__lowerCAmelCase   					=   random_attention_mask([self.batch_size, self.seq_length]							)

																				__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   					=   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  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   List[Any]:
																				__lowerCAmelCase   					=   TFMobileBertModel(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)

																				__lowerCAmelCase   					=   [input_ids, input_mask]
																				__lowerCAmelCase   					=   model(snake_case_							)

																				__lowerCAmelCase   					=   model(snake_case_							)

																				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  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   List[str]:
																				__lowerCAmelCase   					=   TFMobileBertForMaskedLM(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.vocab_size)							)




														def 					A__		(      self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   str:
																				__lowerCAmelCase   					=   TFMobileBertForNextSentencePrediction(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, 2)							)




														def 					A__		(      self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   int:
																				__lowerCAmelCase   					=   TFMobileBertForPreTraining(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				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  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   List[Any]:
																				__lowerCAmelCase   					=   self.num_labels
																				__lowerCAmelCase   					=   TFMobileBertForSequenceClassification(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_labels)							)




														def 					A__		(      self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   Optional[Any]:
																				__lowerCAmelCase   					=   self.num_choices
																				__lowerCAmelCase   					=   TFMobileBertForMultipleChoice(config=snake_case_							)
																				__lowerCAmelCase   					=   tf.tile(tf.expand_dims(snake_case_  ,							1							)  ,							(1, self.num_choices, 1)							)
																				__lowerCAmelCase   					=   tf.tile(tf.expand_dims(snake_case_  ,							1							)  ,							(1, self.num_choices, 1)							)
																				__lowerCAmelCase   					=   tf.tile(tf.expand_dims(snake_case_  ,							1							)  ,							(1, self.num_choices, 1)							)
																				__lowerCAmelCase   					=   {
																				    '''input_ids''': multiple_choice_inputs_ids,
																				    '''attention_mask''': multiple_choice_input_mask,
																				    '''token_type_ids''': multiple_choice_token_type_ids,
																				}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.num_choices)							)




														def 					A__		(      self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   str:
																				__lowerCAmelCase   					=   self.num_labels
																				__lowerCAmelCase   					=   TFMobileBertForTokenClassification(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.seq_length, self.num_labels)							)




														def 					A__		(      self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_							)    ->   Any:
																				__lowerCAmelCase   					=   TFMobileBertForQuestionAnswering(config=snake_case_							)
																				__lowerCAmelCase   					=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
																				__lowerCAmelCase   					=   model(snake_case_							)
																				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							)    ->   str:
																				__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




							def 					A__		(      self							)    ->   str:
													__lowerCAmelCase   					=   TFMobileBertModelTest.TFMobileBertModelTester(self							)
													__lowerCAmelCase   					=   ConfigTester(self  ,							config_class=snake_case_  ,							hidden_size=37							)




							def 					A__		(      self							)    ->   List[Any]:
													self.config_tester.run_common_tests()




							def 					A__		(      self							)    ->   Dict:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_model(*snake_case_							)




							def 					A__		(      self							)    ->   Optional[int]:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_							)




							def 					A__		(      self							)    ->   int:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_							)




							def 					A__		(      self							)    ->   Tuple:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_							)




							def 					A__		(      self							)    ->   Optional[int]:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_							)




							def 					A__		(      self							)    ->   int:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_							)




							def 					A__		(      self							)    ->   Tuple:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_							)




							def 					A__		(      self							)    ->   Optional[int]:
													__lowerCAmelCase   					=   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_							)




							@slow
							def 					A__		(      self							)    ->   Optional[int]:



													for model_name in ["google/mobilebert-uncased"]:
																			__lowerCAmelCase   					=   TFMobileBertModel.from_pretrained(snake_case_							)
																			self.assertIsNotNone(snake_case_							)
@require_tf
class 	lowerCAmelCase_						(							unittest.TestCase      ):



							'''simple docstring'''





							@slow
							def 					A__		(      self							)    ->   Tuple:
													__lowerCAmelCase   					=   TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased"""							)
													__lowerCAmelCase   					=   tf.constant([[0, 1, 2, 3, 4, 5]]							)
													__lowerCAmelCase   					=   model(snake_case_							)[0]

													__lowerCAmelCase   					=   [1, 6, 30_522]
													self.assertEqual(output.shape  ,							snake_case_							)

													__lowerCAmelCase   					=   tf.constant(
													    [
													        [
													            [-4.5_919_547, -9.248_295, -9.645_256],
													            [-6.7_306_175, -6.440_284, -6.6_052_837],
													            [-7.2_743_506, -6.7_847_915, -6.024_673],
													        ]
													    ]							)
													tf.debugging.assert_near(output[:, :3, :3]  ,							snake_case_  ,							atol=1e-4							)







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				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.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				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)
