






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  int	,			__magic_name__					:  Dict="attention"					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :Any          =	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
	UpperCamelCase     :str          =	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
	UpperCamelCase     :Optional[int]          =	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
	UpperCamelCase     :Optional[Any]          =	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
	return k, o, q, v

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  int=False					)		->			Dict:






	"""simple docstring"""



	if split_mlp_wi:
		UpperCamelCase     :List[str]          =	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
		UpperCamelCase     :Tuple          =	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
		UpperCamelCase     :Optional[Any]          =	(wi_a, wi_a)
	else:
		UpperCamelCase     :Union[str, Any]          =	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

	UpperCamelCase     :str          =	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
	return wi, wo

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]	,			__magic_name__					:  Optional[int]	,			__magic_name__					:  str	,			__magic_name__					:  Optional[Any]					)		->			int:






	"""simple docstring"""



	return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  dict	,			*, __magic_name__					:  int	,			__magic_name__					:  bool					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :str          =	traverse_util.flatten_dict(variables["""target"""]					)
	UpperCamelCase     :Optional[int]          =	{"""/""".join(__magic_name__					): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	UpperCamelCase     :List[str]          =	"""encoder/layers_0/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""	,			__magic_name__					)

	UpperCamelCase     :Tuple          =	collections.OrderedDict()

	# Shared embeddings.
	UpperCamelCase     :str          =	old["""token_embedder/embedding"""]

	# Encoder.
	for i in range(__magic_name__					):
		# Block i, layer 0 (Self Attention).
		UpperCamelCase     :int          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""pre_attention_layer_norm"""					)
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""attention"""					)
		UpperCamelCase     :List[str]          =	layer_norm
		UpperCamelCase     :Tuple          =	k.T
		UpperCamelCase     :List[Any]          =	o.T
		UpperCamelCase     :Tuple          =	q.T
		UpperCamelCase     :List[Any]          =	v.T

		# Block i, layer 1 (MLP).
		UpperCamelCase     :str          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""pre_mlp_layer_norm"""					)
		UpperCamelCase     ,      UpperCamelCase     :int          =	tax_mlp_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			__magic_name__					)
		UpperCamelCase     :Optional[int]          =	layer_norm
		if split_mlp_wi:
			UpperCamelCase     :Optional[Any]          =	wi[0].T
			UpperCamelCase     :Optional[Any]          =	wi[1].T
		else:
			UpperCamelCase     :List[str]          =	wi.T
		UpperCamelCase     :Any          =	wo.T

	UpperCamelCase     :Union[str, Any]          =	old[
	    """encoder/relpos_bias/rel_embedding"""
	].T
	UpperCamelCase     :Union[str, Any]          =	old["""encoder/encoder_norm/scale"""]

	if not is_encoder_only:
		# Decoder.
		for i in range(__magic_name__					):
			# Block i, layer 0 (Self Attention).
			UpperCamelCase     :str          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_self_attention_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Any          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""self_attention"""					)
			UpperCamelCase     :int          =	layer_norm
			UpperCamelCase     :List[str]          =	k.T
			UpperCamelCase     :Optional[int]          =	o.T
			UpperCamelCase     :Tuple          =	q.T
			UpperCamelCase     :Union[str, Any]          =	v.T

			# Block i, layer 1 (Cross Attention).
			UpperCamelCase     :Tuple          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_cross_attention_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""encoder_decoder_attention"""					)
			UpperCamelCase     :Tuple          =	layer_norm
			UpperCamelCase     :List[Any]          =	k.T
			UpperCamelCase     :Dict          =	o.T
			UpperCamelCase     :str          =	q.T
			UpperCamelCase     :Optional[Any]          =	v.T

			# Block i, layer 2 (MLP).
			UpperCamelCase     :Tuple          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_mlp_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	tax_mlp_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			__magic_name__					)
			UpperCamelCase     :int          =	layer_norm
			if split_mlp_wi:
				UpperCamelCase     :Dict          =	wi[0].T
				UpperCamelCase     :Union[str, Any]          =	wi[1].T
			else:
				UpperCamelCase     :Tuple          =	wi.T
			UpperCamelCase     :str          =	wo.T

		UpperCamelCase     :Union[str, Any]          =	old["""decoder/decoder_norm/scale"""]
		UpperCamelCase     :str          =	old[
		    """decoder/relpos_bias/rel_embedding"""
		].T

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			UpperCamelCase     :Union[str, Any]          =	old["""decoder/logits_dense/kernel"""].T

	return new

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  bool					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		UpperCamelCase     :Optional[int]          =	state_dict["""shared.weight"""]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			UpperCamelCase     :Dict          =	state_dict["""shared.weight"""]

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print("""Using shared word embeddings as lm_head."""					)
			UpperCamelCase     :List[Any]          =	state_dict["""shared.weight"""]

	return state_dict

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Any	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  List[str]					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	checkpoints.load_tax_checkpoint(__magic_name__					)
	UpperCamelCase     :Tuple          =	convert_tax_to_pytorch(__magic_name__	,			num_layers=config.num_layers	,			is_encoder_only=__magic_name__					)
	UpperCamelCase     :int          =	make_state_dict(__magic_name__	,			__magic_name__					)
	model.load_state_dict(__magic_name__	,			strict=__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  str	,			__magic_name__					:  Any	,			__magic_name__					:  bool = False					)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	TaConfig.from_json_file(__magic_name__					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		UpperCamelCase     :Any          =	TaEncoderModel(__magic_name__					)
	else:
		UpperCamelCase     :str          =	TaForConditionalGeneration(__magic_name__					)

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	model.save_pretrained(__magic_name__					)

	# Verify that we can load the checkpoint.
	model.from_pretrained(__magic_name__					)
	print("""Done"""					)


if __name__ == "__main__":
	UpperCAmelCase_ :						int      =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
	# Required parameters
	parser.add_argument(
	    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
	)
	UpperCAmelCase_ :						Tuple      =      parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Tuple     	=  ShapEImgaImgPipeline
	snake_case__							:     Optional[Any]     	=  ["""image"""]
	snake_case__							:     Union[str, Any]     	=  ["""image"""]
	snake_case__							:     Optional[Any]     	=  [
	    """num_images_per_prompt""",
	    """num_inference_steps""",
	    """generator""",
	    """latents""",
	    """guidance_scale""",
	    """frame_size""",
	    """output_type""",
	    """return_dict""",
	]
	snake_case__							:     List[str]     	=  False





	@property
	def 						_A (		self				:					Any	):
		return 32





	@property
	def 						_A (		self				:					Any	):
		return 32





	@property
	def 						_A (		self				:					Optional[Any]	):
		return self.time_input_dim * 4





	@property
	def 						_A (		self				:					Union[str, Any]	):
		return 8





	@property
	def 						_A (		self				:					int	):
		torch.manual_seed(0	)
		UpperCamelCase     :Union[str, Any]          =	CLIPVisionConfig(
		    hidden_size=self.text_embedder_hidden_size ,  image_size=64 ,  projection_dim=self.text_embedder_hidden_size ,  intermediate_size=37 ,  num_attention_heads=4 ,  num_channels=3 ,  num_hidden_layers=5 ,  patch_size=1 ,  )

		UpperCamelCase     :Optional[int]          =	CLIPVisionModel(__lowerCamelCase	)
		return model





	@property
	def 						_A (		self				:					str	):
		UpperCamelCase     :Optional[int]          =	CLIPImageProcessor(
		    crop_size=224 ,  do_center_crop=__lowerCamelCase ,  do_normalize=__lowerCamelCase ,  do_resize=__lowerCamelCase ,  image_mean=[0.48145466, 0.4578275, 0.40821073] ,  image_std=[0.26862954, 0.26130258, 0.27577711] ,  resample=3 ,  size=224 ,  )

		return image_processor





	@property
	def 						_A (		self				:					Tuple	):
		torch.manual_seed(0	)

		UpperCamelCase     :Dict          =	{
		    """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""",
		    """embedding_proj_norm_type""": """layer""",
		    """encoder_hid_proj_type""": None,
		    """added_emb_type""": None,
		}

		UpperCamelCase     :int          =	PriorTransformer(**__lowerCamelCase	)
		return model





	@property
	def 						_A (		self				:					Optional[int]	):
		torch.manual_seed(0	)

		UpperCamelCase     :str          =	{
		    """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,
		    ),
		}
		UpperCamelCase     :List[str]          =	ShapERenderer(**__lowerCamelCase	)
		return model





	def 						_A (		self				:					str	):
		UpperCamelCase     :int          =	self.dummy_prior
		UpperCamelCase     :Any          =	self.dummy_image_encoder
		UpperCamelCase     :Dict          =	self.dummy_image_processor
		UpperCamelCase     :List[Any]          =	self.dummy_renderer

		UpperCamelCase     :int          =	HeunDiscreteScheduler(
		    beta_schedule="""exp""" ,  num_train_timesteps=1_024 ,  prediction_type="""sample""" ,  use_karras_sigmas=__lowerCamelCase ,  clip_sample=__lowerCamelCase ,  clip_sample_range=1.0 ,  )
		UpperCamelCase     :Optional[Any]          =	{
		    """prior""": prior,
		    """image_encoder""": image_encoder,
		    """image_processor""": image_processor,
		    """renderer""": renderer,
		    """scheduler""": scheduler,
		}

		return components





	def 						_A (		self				:					int ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Any=0	):
		UpperCamelCase     :Any          =	floats_tensor((1, 3, 64, 64) ,  rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

		if str(__lowerCamelCase	).startswith("""mps"""	):
			UpperCamelCase     :List[Any]          =	torch.manual_seed(__lowerCamelCase	)
		else:
			UpperCamelCase     :Optional[int]          =	torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	{
		    """image""": input_image,
		    """generator""": generator,
		    """num_inference_steps""": 1,
		    """frame_size""": 32,
		    """output_type""": """np""",
		}
		return inputs





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Dict          =	"""cpu"""

		UpperCamelCase     :List[Any]          =	self.get_dummy_components()

		UpperCamelCase     :Optional[int]          =	self.pipeline_class(**__lowerCamelCase	)
		UpperCamelCase     :int          =	pipe.to(__lowerCamelCase	)

		pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	pipe(**self.get_dummy_inputs(__lowerCamelCase	)	)
		UpperCamelCase     :Dict          =	output.images[0]
		UpperCamelCase     :List[Any]          =	image[0, -3:, -3:, -1]

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

		UpperCamelCase     :Dict          =	np.array(
		    [
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		        0.00039216,
		    ]	)

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





	def 						_A (		self				:					List[Any]	):
		# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
		self._test_inference_batch_consistent(batch_sizes=[1, 2]	)





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :str          =	torch_device == """cpu"""
		UpperCamelCase     :int          =	True
		self._test_inference_batch_single_identical(
		    batch_size=2 ,  test_max_difference=__lowerCamelCase ,  relax_max_difference=__lowerCamelCase ,  )





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :List[Any]          =	self.get_dummy_components()
		UpperCamelCase     :Optional[int]          =	self.pipeline_class(**__lowerCamelCase	)
		UpperCamelCase     :List[Any]          =	pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Any          =	1
		UpperCamelCase     :int          =	2

		UpperCamelCase     :Union[str, Any]          =	self.get_dummy_inputs(__lowerCamelCase	)

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

		UpperCamelCase     :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      ):





	def 						_A (		self				:					Any	):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def 						_A (		self				:					Any	):
		UpperCamelCase     :Optional[Any]          =	load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""	)
		UpperCamelCase     :Any          =	load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/shap_e/test_shap_e_img2img_out.npy"""	)
		UpperCamelCase     :Union[str, Any]          =	ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""	)
		UpperCamelCase     :List[str]          =	pipe.to(__lowerCamelCase	)
		pipe.set_progress_bar_config(disable=__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	torch.Generator(device=__lowerCamelCase	).manual_seed(0	)

		UpperCamelCase     :Optional[int]          =	pipe(
		    __lowerCamelCase ,  generator=__lowerCamelCase ,  guidance_scale=3.0 ,  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	)
