



"""simple docstring"""






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class  __SCREAMING_SNAKE_CASE			(   _lowerCAmelCase	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							ShapEPipeline
					_a         =							['prompt']
					_a         =							['prompt']
					_a         =							[
					    'num_images_per_prompt',
					    'num_inference_steps',
					    'generator',
					    'latents',
					    'guidance_scale',
					    'frame_size',
					    'output_type',
					    'return_dict',
					]
					_a         =							False



					@property
					def 	snake_case ( self							:						Optional[Any]  )->      Dict:
								return 32



					@property
					def 	snake_case ( self							:						Any  )->      Dict:
								return 32



					@property
					def 	snake_case ( self							:						int  )->      str:
								return self.time_input_dim * 4



					@property
					def 	snake_case ( self							:						Optional[int]  )->      Any:
								return 8



					@property
					def 	snake_case ( self							:						str  )->      Any:
								lowerCamelCase__	:	str				=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
								return tokenizer



					@property
					def 	snake_case ( self							:						str  )->      List[Any]:
								torch.manual_seed(0  )
								lowerCamelCase__	:	List[str]				=CLIPTextConfig(
								    bos_token_id=0,						eos_token_id=2,						hidden_size=self.text_embedder_hidden_size,						projection_dim=self.text_embedder_hidden_size,						intermediate_size=37,						layer_norm_eps=1E-05,						num_attention_heads=4,						num_hidden_layers=5,						pad_token_id=1,						vocab_size=1000,						)
								return CLIPTextModelWithProjection(_lowerCAmelCase  )



					@property
					def 	snake_case ( self							:						Any  )->      str:
								torch.manual_seed(0  )

								lowerCamelCase__	:	Optional[int]				={
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 16,
								    '''embedding_dim''': self.time_input_dim,
								    '''num_embeddings''': 32,
								    '''embedding_proj_dim''': self.text_embedder_hidden_size,
								    '''time_embed_dim''': self.time_embed_dim,
								    '''num_layers''': 1,
								    '''clip_embed_dim''': self.time_input_dim * 2,
								    '''additional_embeddings''': 0,
								    '''time_embed_act_fn''': '''gelu''',
								    '''norm_in_type''': '''layer''',
								    '''encoder_hid_proj_type''': None,
								    '''added_emb_type''': None,
								}

								lowerCamelCase__	:	str				=PriorTransformer(**_lowerCAmelCase  )
								return model



					@property
					def 	snake_case ( self							:						Optional[Any]  )->      Optional[int]:
								torch.manual_seed(0  )

								lowerCamelCase__	:	Any				={
								    '''param_shapes''': (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    '''d_latent''': self.time_input_dim,
								    '''d_hidden''': self.renderer_dim,
								    '''n_output''': 12,
								    '''background''': (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								lowerCamelCase__	:	int				=ShapERenderer(**_lowerCAmelCase  )
								return model



					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	str				=self.dummy_prior
								lowerCamelCase__	:	str				=self.dummy_text_encoder
								lowerCamelCase__	:	Dict				=self.dummy_tokenizer
								lowerCamelCase__	:	str				=self.dummy_renderer

								lowerCamelCase__	:	Optional[Any]				=HeunDiscreteScheduler(
								    beta_schedule='''exp''',						num_train_timesteps=1024,						prediction_type='''sample''',						use_karras_sigmas=_lowerCAmelCase,						clip_sample=_lowerCAmelCase,						clip_sample_range=1.0,						)
								lowerCamelCase__	:	Union[str, Any]				={
								    '''prior''': prior,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''renderer''': renderer,
								    '''scheduler''': scheduler,
								}

								return components



					def 	snake_case ( self							:						Dict,						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Any=0  )->      List[Any]:







								if str(_lowerCAmelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	Optional[int]				=torch.manual_seed(_lowerCAmelCase  )
								else:
											lowerCamelCase__	:	Optional[int]				=torch.Generator(device=_lowerCAmelCase  ).manual_seed(_lowerCAmelCase  )
								lowerCamelCase__	:	Optional[Any]				={
								    '''prompt''': '''horse''',
								    '''generator''': generator,
								    '''num_inference_steps''': 1,
								    '''frame_size''': 32,
								    '''output_type''': '''np''',
								}
								return inputs



					def 	snake_case ( self							:						Tuple  )->      Dict:
								lowerCamelCase__	:	List[Any]				='''cpu'''

								lowerCamelCase__	:	Optional[Any]				=self.get_dummy_components()

								lowerCamelCase__	:	Union[str, Any]				=self.pipeline_class(**_lowerCAmelCase  )
								lowerCamelCase__	:	List[str]				=pipe.to(_lowerCAmelCase  )

								pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

								lowerCamelCase__	:	int				=pipe(**self.get_dummy_inputs(_lowerCAmelCase  )  )
								lowerCamelCase__	:	int				=output.images[0]
								lowerCamelCase__	:	Tuple				=image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								lowerCamelCase__	:	Tuple				=np.array(
								    [
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								        0.00_039_216,
								    ]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2



					def 	snake_case ( self							:						Optional[Any]  )->      Optional[Any]:
								self._test_inference_batch_consistent(batch_sizes=[1, 2]  )



					def 	snake_case ( self							:						Optional[int]  )->      str:
								lowerCamelCase__	:	List[Any]				=torch_device == '''cpu'''
								lowerCamelCase__	:	List[str]				=True

								self._test_inference_batch_single_identical(
								    batch_size=2,						test_max_difference=_lowerCAmelCase,						relax_max_difference=_lowerCAmelCase,						)




					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	str				=self.get_dummy_components()
								lowerCamelCase__	:	Tuple				=self.pipeline_class(**_lowerCAmelCase  )
								lowerCamelCase__	:	int				=pipe.to(_lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

								lowerCamelCase__	:	Dict				=1
								lowerCamelCase__	:	List[Any]				=2

								lowerCamelCase__	:	List[str]				=self.get_dummy_inputs(_lowerCAmelCase  )

								for key in inputs.keys():
											if key in self.batch_params:
														lowerCamelCase__	:	Tuple				=batch_size * [inputs[key]]

								lowerCamelCase__	:	Optional[int]				=pipe(**_lowerCAmelCase,						num_images_per_prompt=_lowerCAmelCase  )[0]

								assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								lowerCamelCase__	:	str				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/shap_e/test_shap_e_np_out.npy'''  )
								lowerCamelCase__	:	List[str]				=ShapEPipeline.from_pretrained('''openai/shap-e'''  )
								lowerCamelCase__	:	List[Any]				=pipe.to(_lowerCAmelCase  )
								pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

								lowerCamelCase__	:	Dict				=torch.Generator(device=_lowerCAmelCase  ).manual_seed(0  )

								lowerCamelCase__	:	Union[str, Any]				=pipe(
								    '''a shark''',						generator=_lowerCAmelCase,						guidance_scale=15.0,						num_inference_steps=64,						frame_size=64,						output_type='''np''',						).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(_lowerCAmelCase,						_lowerCAmelCase  )










"""simple docstring"""






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=AutoConfig.from_pretrained(__lowerCamelCase			)
			lowerCamelCase__	:	Any				=FlaxAutoModelForSeqaSeqLM.from_config(config=__lowerCamelCase			)
			lowerCamelCase__	:	Union[str, Any]				=checkpoints.load_tax_checkpoint(__lowerCamelCase			)

			lowerCamelCase__	:	Union[str, Any]				='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			if config.model_type == "t5":
						lowerCamelCase__	:	List[str]				='''SelfAttention'''
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						lowerCamelCase__	:	List[Any]				='''LocalSelfAttention'''
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[Any]				='''TransientGlobalSelfAttention'''
			else:
						raise ValueError(
						    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
						    ''' attribute with a value from [\'local\', \'transient-global].'''			)

			# Encoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	List[Any]				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	str				=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

						# Layer Normalization
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Dict				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[str]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Tuple				=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''encoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	int				=tax_attention_key
						lowerCamelCase__	:	Optional[int]				=tax_attention_out
						lowerCamelCase__	:	List[Any]				=tax_attention_query
						lowerCamelCase__	:	Optional[Any]				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_attention_layer_norm

						# Global input layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
									lowerCamelCase__	:	Optional[int]				=tax_global_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
									lowerCamelCase__	:	Optional[int]				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	Union[str, Any]				=tax_mlp_wi

						lowerCamelCase__	:	str				=tax_mlp_wo
						lowerCamelCase__	:	Optional[Any]				=tax_mlp_layer_norm

						lowerCamelCase__	:	Optional[int]				=flax_model_encoder_layer_block

			# Only for layer 0:
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	str				=tax_encoder_rel_embedding

			# Side/global relative position_bias + layer norm
			if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
						lowerCamelCase__	:	Optional[int]				=tax_encoder_global_rel_embedding

			# Assigning
			lowerCamelCase__	:	int				=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			lowerCamelCase__	:	List[Any]				=tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers			):
						lowerCamelCase__	:	Dict				=f'''layers_{str(__lowerCamelCase			)}'''

						# Self-Attention
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						lowerCamelCase__	:	Optional[int]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
						lowerCamelCase__	:	List[Any]				=tax_enc_dec_attention_module['''key''']['''kernel''']
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_module['''out''']['''kernel''']
						lowerCamelCase__	:	Dict				=tax_enc_dec_attention_module['''query''']['''kernel''']
						lowerCamelCase__	:	List[str]				=tax_enc_dec_attention_module['''value''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									lowerCamelCase__	:	Any				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									lowerCamelCase__	:	List[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						lowerCamelCase__	:	Optional[Any]				=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						lowerCamelCase__	:	str				=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						lowerCamelCase__	:	str				=flax_model.params['''decoder''']['''block'''][str(__lowerCamelCase			)]['''layer''']
						lowerCamelCase__	:	Union[str, Any]				=tax_attention_key
						lowerCamelCase__	:	str				=tax_attention_out
						lowerCamelCase__	:	Optional[int]				=tax_attention_query
						lowerCamelCase__	:	Dict				=tax_attention_value

						lowerCamelCase__	:	List[str]				=tax_pre_attention_layer_norm

						lowerCamelCase__	:	List[Any]				=tax_enc_dec_attention_key
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_out
						lowerCamelCase__	:	Any				=tax_enc_dec_attention_query
						lowerCamelCase__	:	Optional[int]				=tax_enc_dec_attention_value

						lowerCamelCase__	:	Dict				=tax_cross_layer_norm

						if split_mlp_wi:
									lowerCamelCase__	:	Tuple				=tax_mlp_wi_a
									lowerCamelCase__	:	int				=tax_mlp_wi_a
						else:
									lowerCamelCase__	:	List[Any]				=tax_mlp_wi

						lowerCamelCase__	:	Dict				=tax_mlp_wo

						lowerCamelCase__	:	Tuple				=txa_mlp_layer_norm

						lowerCamelCase__	:	Optional[Any]				=flax_model_decoder_layer_block

			# Decoder Normalization
			lowerCamelCase__	:	Dict				=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			lowerCamelCase__	:	int				=txa_decoder_norm

			# Only for layer 0:
			lowerCamelCase__	:	Tuple				=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
			lowerCamelCase__	:	Tuple				=tax_decoder_rel_embedding

			# Token Embeddings
			lowerCamelCase__	:	Union[str, Any]				=tax_model['''target''']['''token_embedder''']['''embedding''']
			lowerCamelCase__	:	Dict				=txa_token_embeddings

			# LM Head (only in v1.1 and LongT5 checkpoints)
			if "logits_dense" in tax_model["target"]["decoder"]:
						lowerCamelCase__	:	int				=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

			flax_model.save_pretrained(__lowerCamelCase			)
			print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
		_lowercase				:				Tuple   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
		)
		parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
		parser.add_argument(
		    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
		)
		_lowercase				:				List[Any]   		=		parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




