



"""simple docstring"""






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard

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


					'''simple docstring'''





					def 	snake_case ( self							:						List[Any]  )->      Optional[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()



					def 	snake_case ( self							:						Optional[Any]  )->      Tuple:
								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-canny''',						from_pt=lowerCamelCase,						dtype=jnp.bfloataa  )
								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',						controlnet=lowerCamelCase,						from_pt=lowerCamelCase,						dtype=jnp.bfloataa  )
								lowerCamelCase__	:	Union[str, Any]				=controlnet_params

								lowerCamelCase__	:	Dict				='''bird'''
								lowerCamelCase__	:	Optional[Any]				=jax.device_count()
								lowerCamelCase__	:	int				=pipe.prepare_text_inputs([prompts] * num_samples  )

								lowerCamelCase__	:	Optional[Any]				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''  )
								lowerCamelCase__	:	List[str]				=pipe.prepare_image_inputs([canny_image] * num_samples  )

								lowerCamelCase__	:	List[Any]				=jax.random.PRNGKey(0  )
								lowerCamelCase__	:	Union[str, Any]				=jax.random.split(lowerCamelCase,						jax.device_count()  )

								lowerCamelCase__	:	Any				=replicate(lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=shard(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=shard(lowerCamelCase  )

								lowerCamelCase__	:	List[str]				=pipe(
								    prompt_ids=lowerCamelCase,						image=lowerCamelCase,						params=lowerCamelCase,						prng_seed=lowerCamelCase,						num_inference_steps=50,						jit=lowerCamelCase,						).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__	:	int				=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
								lowerCamelCase__	:	Optional[Any]				=images[0, 253:256, 253:256, -1]

								lowerCamelCase__	:	Optional[Any]				=jnp.asarray(jax.device_get(image_slice.flatten()  )  )
								lowerCamelCase__	:	List[Any]				=jnp.array(
								    [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]  )
								print(F'''output_slice: {output_slice}'''  )
								assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2




					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=FlaxControlNetModel.from_pretrained(
								    '''lllyasviel/sd-controlnet-openpose''',						from_pt=lowerCamelCase,						dtype=jnp.bfloataa  )
								lowerCamelCase__			,     lowerCamelCase__	:	int				=FlaxStableDiffusionControlNetPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-v1-5''',						controlnet=lowerCamelCase,						from_pt=lowerCamelCase,						dtype=jnp.bfloataa  )
								lowerCamelCase__	:	List[str]				=controlnet_params

								lowerCamelCase__	:	Union[str, Any]				='''Chef in the kitchen'''
								lowerCamelCase__	:	List[Any]				=jax.device_count()
								lowerCamelCase__	:	int				=pipe.prepare_text_inputs([prompts] * num_samples  )

								lowerCamelCase__	:	int				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''  )
								lowerCamelCase__	:	Optional[Any]				=pipe.prepare_image_inputs([pose_image] * num_samples  )

								lowerCamelCase__	:	Optional[int]				=jax.random.PRNGKey(0  )
								lowerCamelCase__	:	int				=jax.random.split(lowerCamelCase,						jax.device_count()  )

								lowerCamelCase__	:	List[Any]				=replicate(lowerCamelCase  )
								lowerCamelCase__	:	int				=shard(lowerCamelCase  )
								lowerCamelCase__	:	str				=shard(lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=pipe(
								    prompt_ids=lowerCamelCase,						image=lowerCamelCase,						params=lowerCamelCase,						prng_seed=lowerCamelCase,						num_inference_steps=50,						jit=lowerCamelCase,						).images
								assert images.shape == (jax.device_count(), 1, 768, 512, 3)

								lowerCamelCase__	:	Tuple				=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
								lowerCamelCase__	:	Any				=images[0, 253:256, 253:256, -1]

								lowerCamelCase__	:	Any				=jnp.asarray(jax.device_get(image_slice.flatten()  )  )
								lowerCamelCase__	:	Dict				=jnp.array(
								    [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]  )
								print(F'''output_slice: {output_slice}'''  )
								assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2









"""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)




