



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_													=      False
lowercase_													=      True
lowercase_													=      False


if __name__ == "__main__":
				lowercase_													=      argparse.ArgumentParser()

				parser.add_argument(
				    '''--repo_path''',
				    default=None,
				    type=str,
				    required=True,
				    help='''The config json file corresponding to the architecture.''',
				)

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

				lowercase_													=      parser.parse_args()

				lowercase_													=      {
				    '''image_size''': '''sample_size''',
				    '''num_res_blocks''': '''layers_per_block''',
				    '''block_channels''': '''block_out_channels''',
				    '''down_blocks''': '''down_block_types''',
				    '''up_blocks''': '''up_block_types''',
				    '''downscale_freq_shift''': '''freq_shift''',
				    '''resnet_num_groups''': '''norm_num_groups''',
				    '''resnet_act_fn''': '''act_fn''',
				    '''resnet_eps''': '''norm_eps''',
				    '''num_head_channels''': '''attention_head_dim''',
				}

				lowercase_													=      {
				    '''time_steps''': '''time_proj''',
				    '''mid''': '''mid_block''',
				    '''downsample_blocks''': '''down_blocks''',
				    '''upsample_blocks''': '''up_blocks''',
				}

				lowercase_													=      '''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

				with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
								lowercase_													=      reader.read()
								lowercase_													=      json.loads(text)

				if do_only_config:
								for key in config_parameters_to_change.keys():
												config.pop(key, None)

				if has_file(args.repo_path, '''config.json'''):
								lowercase_													=      UNetaDModel(**config)
				else:
								lowercase_													=      UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
								lowercase_													=      class_name(**config)

				if do_only_config:
								model.save_config(os.path.join(args.repo_path, subfolder))

				lowercase_													=      dict(model.config)

				if do_only_renaming:
								for key, value in config_parameters_to_change.items():
												if key in config:
																lowercase_													=      config[key]
																del config[key]

								lowercase_													=      [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
								lowercase_													=      [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

				if do_only_weights:
								lowercase_													=      torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

								lowercase_													=      {}
								for param_key, param_value in state_dict.items():
												if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
																continue
												lowercase_													=      False
												for key, new_key in key_parameters_to_change.items():
																if not has_changed and param_key.split('''.''')[0] == key:
																				lowercase_													=      param_value
																				lowercase_													=      True
												if not has_changed:
																lowercase_													=      param_value

								model.load_state_dict(new_state_dict)
								model.save_pretrained(os.path.join(args.repo_path, subfolder))









from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

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


if is_tf_available():
				import tensorflow as tf

				from transformers import TFBlipTextModel
				from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class     __a			:



	def __init__(    self				:     int					,						snake_case_				:     Dict					,						snake_case_				:     List[str]=12					,						snake_case_				:     Optional[int]=7					,						snake_case_				:     Optional[Any]=True					,						snake_case_				:     int=True					,						snake_case_				:     int=True					,						snake_case_				:     Any=99					,						snake_case_				:     List[Any]=32					,						snake_case_				:     List[Any]=32					,						snake_case_				:     Any=2					,						snake_case_				:     Optional[Any]=4					,						snake_case_				:     Optional[Any]=37					,						snake_case_				:     Tuple=0.1					,						snake_case_				:     str=0.1					,						snake_case_				:     Any=5_12					,						snake_case_				:     Optional[Any]=0.0_2					,						snake_case_				:     List[Any]=0					,						snake_case_				:     Union[str, Any]=None					,						)->				List[Any]:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =seq_length
						__lowerCAmelCase						 =is_training
						__lowerCAmelCase						 =use_input_mask
						__lowerCAmelCase						 =use_labels
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =projection_dim
						__lowerCAmelCase						 =num_hidden_layers
						__lowerCAmelCase						 =num_attention_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =scope
						__lowerCAmelCase						 =bos_token_id



	def 		UpperCamelCase				(    self				:     List[Any])->				Optional[int]:
						__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])

						if input_mask is not None:
											__lowerCAmelCase						 =input_mask.numpy()
											__lowerCAmelCase ,						__lowerCAmelCase						 =input_mask.shape
											__lowerCAmelCase						 =np.random.randint(1					,						seq_length - 1					,						size=(batch_size,))
											for batch_idx, start_index in enumerate(snake_case_):
																__lowerCAmelCase						 =1
																__lowerCAmelCase						 =0

						__lowerCAmelCase						 =self.get_config()

						return config, input_ids, tf.convert_to_tensor(snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				str:
						return BlipTextConfig(
						    vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						projection_dim=self.projection_dim					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						dropout=self.dropout					,						attention_dropout=self.attention_dropout					,						max_position_embeddings=self.max_position_embeddings					,						initializer_range=self.initializer_range					,						bos_token_id=self.bos_token_id					,						)



	def 		UpperCamelCase				(    self				:     Union[str, Any]					,						snake_case_				:     Tuple					,						snake_case_				:     List[Any]					,						snake_case_				:     List[Any])->				List[str]:
						__lowerCAmelCase						 =TFBlipTextModel(config=snake_case_)
						__lowerCAmelCase						 =model(snake_case_					,						attention_mask=snake_case_					,						training=snake_case_)
						__lowerCAmelCase						 =model(snake_case_					,						training=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 		UpperCamelCase				(    self				:     Dict)->				Tuple:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =config_and_inputs
						__lowerCAmelCase						 ={"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict






@require_tf
class     __a			(  SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(TFBlipTextModel,) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False



	def 		UpperCamelCase				(    self				:     Optional[Any])->				Any:
						__lowerCAmelCase						 =BlipTextModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=snake_case_					,						hidden_size=37)



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



	def 		UpperCamelCase				(    self				:     List[str])->				Dict:
						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						pass



	def 		UpperCamelCase				(    self				:     List[str])->				int:
						pass



	@unittest.skip(reason="""Blip does not use inputs_embeds""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				int:
						pass



	@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING""")
	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						pass



	@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						pass



	@slow
	def 		UpperCamelCase				(    self				:     int)->				str:
						for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase						 =TFBlipTextModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     List[str]=True)->				int:
						super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case_)




