


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[Any]:
				return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_="attention" )-> List[Any]:
				lowerCAmelCase_ :      int    				=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :] )
				lowerCAmelCase_ :      Tuple    				=				k_tmp.reshape(k_tmp.shape[0] ,   k_tmp.shape[1] * k_tmp.shape[2] )
				lowerCAmelCase_ :      int    				=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :] )
				lowerCAmelCase_ :      Dict    				=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,   o_tmp.shape[2] )
				lowerCAmelCase_ :      Any    				=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :] )
				lowerCAmelCase_ :      Optional[int]    				=				q_tmp.reshape(q_tmp.shape[0] ,   q_tmp.shape[1] * q_tmp.shape[2] )
				lowerCAmelCase_ :      int    				=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :] )
				lowerCAmelCase_ :      Optional[Any]    				=				v_tmp.reshape(v_tmp.shape[0] ,   v_tmp.shape[1] * v_tmp.shape[2] )
				return k, o, q, v



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_=False )-> Any:
				if split_mlp_wi:
								lowerCAmelCase_ :      List[Any]    				=				params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
								lowerCAmelCase_ :      Optional[int]    				=				params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
								lowerCAmelCase_ :      Optional[Any]    				=				(wi_a, wi_a)
				else:
								lowerCAmelCase_ :      List[str]    				=				params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

				lowerCAmelCase_ :      int    				=				params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
				return wi, wo



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Union[str, Any]:
				return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 			lowerCAmelCase (		lowerCAmelCase_ ,   *, lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = False )-> Dict:
				lowerCAmelCase_ :      Optional[int]    				=				traverse_util.flatten_dict(variables['''target'''] )
				lowerCAmelCase_ :      List[Any]    				=				{'''/'''.join(lowerCAmelCase_ ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				lowerCAmelCase_ :      Dict    				=				'''encoder/encoder/mlp/wi_0/kernel''' in old
				print('''Split MLP:''' ,   lowerCAmelCase_ )

				lowerCAmelCase_ :      Dict    				=				collections.OrderedDict()

				# Shared embeddings.
				lowerCAmelCase_ :      Union[str, Any]    				=				old['''token_embedder/embedding''']

				# Encoder.
				for i in range(lowerCAmelCase_ ):
								# Block i, layer 0 (Self Attention).
								lowerCAmelCase_ :      Tuple    				=				tax_layer_norm_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''encoder''' ,   '''pre_attention_layer_norm''' )
								lowerCAmelCase_ :      Tuple    				=				tax_attention_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''encoder''' ,   '''attention''' )
								lowerCAmelCase_ :      Any    				=				layer_norm
								lowerCAmelCase_ :      int    				=				k.T
								lowerCAmelCase_ :      Tuple    				=				o.T
								lowerCAmelCase_ :      Dict    				=				q.T
								lowerCAmelCase_ :      Union[str, Any]    				=				v.T

								# Block i, layer 1 (MLP).
								lowerCAmelCase_ :      Any    				=				tax_layer_norm_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''encoder''' ,   '''pre_mlp_layer_norm''' )
								lowerCAmelCase_ :      List[str]    				=				tax_mlp_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''encoder''' ,   lowerCAmelCase_ )
								lowerCAmelCase_ :      Union[str, Any]    				=				layer_norm
								if split_mlp_wi:
												lowerCAmelCase_ :      Optional[Any]    				=				wi[0].T
												lowerCAmelCase_ :      Optional[int]    				=				wi[1].T
								else:
												lowerCAmelCase_ :      int    				=				wi.T
								lowerCAmelCase_ :      Dict    				=				wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												lowerCAmelCase_ :      Dict    				=				tax_relpos_bias_lookup(
												    lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''encoder''' ).T

				lowerCAmelCase_ :      Tuple    				=				old['''encoder/encoder_norm/scale''']

				if not scalable_attention:
								lowerCAmelCase_ :      Dict    				=				tax_relpos_bias_lookup(
								    lowerCAmelCase_ ,   0 ,   '''encoder''' ).T
								lowerCAmelCase_ :      Any    				=				tax_relpos_bias_lookup(
								    lowerCAmelCase_ ,   0 ,   '''decoder''' ).T

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

												# Block i, layer 1 (Cross Attention).
												lowerCAmelCase_ :      Union[str, Any]    				=				tax_layer_norm_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''decoder''' ,   '''pre_cross_attention_layer_norm''' )
												lowerCAmelCase_ :      str    				=				tax_attention_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''decoder''' ,   '''encoder_decoder_attention''' )
												lowerCAmelCase_ :      Tuple    				=				layer_norm
												lowerCAmelCase_ :      List[str]    				=				k.T
												lowerCAmelCase_ :      Dict    				=				o.T
												lowerCAmelCase_ :      int    				=				q.T
												lowerCAmelCase_ :      int    				=				v.T

												# Block i, layer 2 (MLP).
												lowerCAmelCase_ :      Any    				=				tax_layer_norm_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''decoder''' ,   '''pre_mlp_layer_norm''' )
												lowerCAmelCase_ :      Union[str, Any]    				=				tax_mlp_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''decoder''' ,   lowerCAmelCase_ )
												lowerCAmelCase_ :      Dict    				=				layer_norm
												if split_mlp_wi:
																lowerCAmelCase_ :      List[str]    				=				wi[0].T
																lowerCAmelCase_ :      Union[str, Any]    				=				wi[1].T
												else:
																lowerCAmelCase_ :      Union[str, Any]    				=				wi.T
												lowerCAmelCase_ :      Union[str, Any]    				=				wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																lowerCAmelCase_ :      int    				=				tax_relpos_bias_lookup(lowerCAmelCase_ ,   lowerCAmelCase_ ,   '''decoder''' ).T

								lowerCAmelCase_ :      int    				=				old['''decoder/decoder_norm/scale''']

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												lowerCAmelCase_ :      Union[str, Any]    				=				old['''decoder/logits_dense/kernel'''].T

				return new



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Dict    				=				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:
								lowerCAmelCase_ :      Tuple    				=				state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowerCAmelCase_ :      Union[str, Any]    				=				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.''' )
												lowerCAmelCase_ :      Tuple    				=				state_dict['''shared.weight''']

				return state_dict



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Tuple:
				lowerCAmelCase_ :      Union[str, Any]    				=				checkpoints.load_tax_checkpoint(lowerCAmelCase_ )
				lowerCAmelCase_ :      Tuple    				=				convert_tax_to_pytorch(
				    lowerCAmelCase_ ,   num_layers=config.num_layers ,   is_encoder_only=lowerCAmelCase_ ,   scalable_attention=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[int]    				=				make_state_dict(lowerCAmelCase_ ,   lowerCAmelCase_ )
				model.load_state_dict(lowerCAmelCase_ ,   strict=lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ = False ,   lowerCAmelCase_ = False ,   )-> Optional[Any]:
				lowerCAmelCase_ :      Union[str, Any]    				=				MTaConfig.from_json_file(lowerCAmelCase_ )
				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:
								lowerCAmelCase_ :      Tuple    				=				UMTaEncoderModel(lowerCAmelCase_ )
				else:
								lowerCAmelCase_ :      Tuple    				=				UMTaForConditionalGeneration(lowerCAmelCase_ )

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

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

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowerCAmelCase_ )
				print('''Done''' )


if __name__ == "__main__":
			_UpperCAmelCase			:  Dict     =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
			)
			parser.add_argument(
			    """--scalable_attention""",
			    action="""store_true""",
			    help="""Whether the model uses scaled attention (umt5 model)""",
			    default=False,
			)
			_UpperCAmelCase			:  Optional[Any]     =parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)



import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_UpperCAmelCase			:  Any     =False







class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    Optional[int]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						@property
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return 1_2




						@property
						def 						lowercase_ (				self							)					->    Any:
										return 1_2




						@property
						def 						lowercase_ (				self							)					->    Optional[Any]:
										return 3_2




						@property
						def 						lowercase_ (				self							)					->    int:
										torch.manual_seed(0							)
										lowerCAmelCase_ :      Any    				=				VQModel(
										    block_out_channels=[3_2, 6_4]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,						latent_channels=3   ,						num_vq_embeddings=self.num_embed   ,						vq_embed_dim=3   ,						)
										return model




						@property
						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      Optional[Any]    				=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)
										return tokenizer




						@property
						def 						lowercase_ (				self							)					->    int:
										torch.manual_seed(0							)
										lowerCAmelCase_ :      Union[str, Any]    				=				CLIPTextConfig(
										    bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=self.text_embedder_hidden_size   ,						intermediate_size=3_7   ,						layer_norm_eps=1e-05   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1_0_0_0   ,						)
										return CLIPTextModel(__lowercase							)




						@property
						def 						lowercase_ (				self							)					->    List[str]:
										torch.manual_seed(0							)

										lowerCAmelCase_ :      Union[str, Any]    				=				1_2
										lowerCAmelCase_ :      int    				=				1_2

										lowerCAmelCase_ :      Union[str, Any]    				=				{
										    '''attention_bias''': True,
										    '''cross_attention_dim''': 3_2,
										    '''attention_head_dim''': height * width,
										    '''num_attention_heads''': 1,
										    '''num_vector_embeds''': self.num_embed,
										    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
										    '''norm_num_groups''': 3_2,
										    '''sample_size''': width,
										    '''activation_fn''': '''geglu-approximate''',
										}

										lowerCAmelCase_ :      List[str]    				=				TransformeraDModel(**__lowercase							)
										return model




						def 						lowercase_ (				self							)					->    str:
										lowerCAmelCase_ :      List[Any]    				=				'''cpu'''

										lowerCAmelCase_ :      Any    				=				self.dummy_vqvae
										lowerCAmelCase_ :      str    				=				self.dummy_text_encoder
										lowerCAmelCase_ :      Union[str, Any]    				=				self.dummy_tokenizer
										lowerCAmelCase_ :      int    				=				self.dummy_transformer
										lowerCAmelCase_ :      List[str]    				=				VQDiffusionScheduler(self.num_embed							)
										lowerCAmelCase_ :      Union[str, Any]    				=				LearnedClassifierFreeSamplingEmbeddings(learnable=__lowercase							)

										lowerCAmelCase_ :      Dict    				=				VQDiffusionPipeline(
										    vqvae=__lowercase   ,						text_encoder=__lowercase   ,						tokenizer=__lowercase   ,						transformer=__lowercase   ,						scheduler=__lowercase   ,						learned_classifier_free_sampling_embeddings=__lowercase   ,						)
										lowerCAmelCase_ :      int    				=				pipe.to(__lowercase							)
										pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Any    				=				'''teddy bear playing in the pool'''

										lowerCAmelCase_ :      int    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Tuple    				=				pipe([prompt]   ,						generator=__lowercase   ,						num_inference_steps=2   ,						output_type='''np'''							)
										lowerCAmelCase_ :      Union[str, Any]    				=				output.images

										lowerCAmelCase_ :      List[Any]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      List[Any]    				=				pipe(
										    [prompt]   ,						generator=__lowercase   ,						output_type='''np'''   ,						return_dict=__lowercase   ,						num_inference_steps=2							)[0]

										lowerCAmelCase_ :      List[str]    				=				image[0, -3:, -3:, -1]
										lowerCAmelCase_ :      str    				=				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 2_4, 2_4, 3)

										lowerCAmelCase_ :      Optional[int]    				=				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]							)

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




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      Optional[Any]    				=				'''cpu'''

										lowerCAmelCase_ :      str    				=				self.dummy_vqvae
										lowerCAmelCase_ :      Dict    				=				self.dummy_text_encoder
										lowerCAmelCase_ :      List[Any]    				=				self.dummy_tokenizer
										lowerCAmelCase_ :      Union[str, Any]    				=				self.dummy_transformer
										lowerCAmelCase_ :      Tuple    				=				VQDiffusionScheduler(self.num_embed							)
										lowerCAmelCase_ :      str    				=				LearnedClassifierFreeSamplingEmbeddings(
										    learnable=__lowercase   ,						hidden_size=self.text_embedder_hidden_size   ,						length=tokenizer.model_max_length							)

										lowerCAmelCase_ :      List[str]    				=				VQDiffusionPipeline(
										    vqvae=__lowercase   ,						text_encoder=__lowercase   ,						tokenizer=__lowercase   ,						transformer=__lowercase   ,						scheduler=__lowercase   ,						learned_classifier_free_sampling_embeddings=__lowercase   ,						)
										lowerCAmelCase_ :      Union[str, Any]    				=				pipe.to(__lowercase							)
										pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Any    				=				'''teddy bear playing in the pool'''

										lowerCAmelCase_ :      List[str]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Dict    				=				pipe([prompt]   ,						generator=__lowercase   ,						num_inference_steps=2   ,						output_type='''np'''							)
										lowerCAmelCase_ :      str    				=				output.images

										lowerCAmelCase_ :      List[Any]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Union[str, Any]    				=				pipe(
										    [prompt]   ,						generator=__lowercase   ,						output_type='''np'''   ,						return_dict=__lowercase   ,						num_inference_steps=2							)[0]

										lowerCAmelCase_ :      List[str]    				=				image[0, -3:, -3:, -1]
										lowerCAmelCase_ :      str    				=				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 2_4, 2_4, 3)

										lowerCAmelCase_ :      Union[str, Any]    				=				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







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







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    Optional[int]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Tuple    				=				load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''							)

										lowerCAmelCase_ :      str    				=				VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''							)
										lowerCAmelCase_ :      List[Any]    				=				pipeline.to(__lowercase							)
										pipeline.set_progress_bar_config(disable=__lowercase							)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										lowerCAmelCase_ :      List[Any]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Optional[int]    				=				pipeline(
										    '''teddy bear playing in the pool'''   ,						num_images_per_prompt=1   ,						generator=__lowercase   ,						output_type='''np'''   ,						)

										lowerCAmelCase_ :      Union[str, Any]    				=				output.images[0]

										assert image.shape == (2_5_6, 2_5_6, 3)
										assert np.abs(expected_image - image							).max() < 2.0