





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 			A_ (a_  ,							a_  ,							unittest.TestCase    ):
	UpperCAmelCase__      =    StableDiffusionParadigmsPipeline
	UpperCAmelCase__      =    TEXT_TO_IMAGE_PARAMS
	UpperCAmelCase__      =    TEXT_TO_IMAGE_BATCH_PARAMS
	UpperCAmelCase__      =    TEXT_TO_IMAGE_IMAGE_PARAMS
	UpperCAmelCase__      =    TEXT_TO_IMAGE_IMAGE_PARAMS
	def   _lowercase       (					self	):






				'''simple docstring'''







				torch.manual_seed(0	)
				UpperCAmelCase     	=       UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=4							,      out_channels=4							,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,      cross_attention_dim=3_2							,      attention_head_dim=(2, 4)							,      use_linear_projection=_A							,      )
				UpperCAmelCase     	=       DDIMParallelScheduler(
				    beta_start=0.0_00_85							,      beta_end=0.0_12							,      beta_schedule='''scaled_linear'''							,      clip_sample=_A							,      set_alpha_to_one=_A							,      )
				torch.manual_seed(0	)
				UpperCAmelCase     	=       AutoencoderKL(
				    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=4							,      sample_size=1_2_8							,      )
				torch.manual_seed(0	)
				UpperCAmelCase     	=       CLIPTextConfig(
				    bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      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							,      hidden_act='''gelu'''							,      projection_dim=5_1_2							,      )
				UpperCAmelCase     	=       CLIPTextModel(_A	)
				UpperCAmelCase     	=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

				UpperCAmelCase     	=       {
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components
	def   _lowercase       (					self							,      _A							,      _A=0	):






				'''simple docstring'''







				if str(_A	).startswith('''mps'''	):
							UpperCAmelCase     	=       torch.manual_seed(_A	)
				else:
							UpperCAmelCase     	=       torch.Generator(device=_A	).manual_seed(_A	)
				UpperCAmelCase     	=       {
				    '''prompt''': '''a photograph of an astronaut riding a horse''',
				    '''generator''': generator,
				    '''num_inference_steps''': 1_0,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''parallel''': 3,
				    '''debug''': True,
				}
				return inputs
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase     	=       self.get_dummy_components()
				UpperCAmelCase     	=       StableDiffusionParadigmsPipeline(**_A	)
				UpperCAmelCase     	=       sd_pipe.to(_A	)
				sd_pipe.set_progress_bar_config(disable=_A	)

				UpperCAmelCase     	=       self.get_dummy_inputs(_A	)
				UpperCAmelCase     	=       sd_pipe(**_A	).images
				UpperCAmelCase     	=       image[0, -3:, -3:, -1]
				assert image.shape == (1, 6_4, 6_4, 3)

				UpperCAmelCase     	=       np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase     	=       self.get_dummy_components()
				torch.manual_seed(0	)
				UpperCAmelCase     	=       DDPMParallelScheduler()
				torch.manual_seed(0	)
				UpperCAmelCase     	=       StableDiffusionParadigmsPipeline(**_A	)
				UpperCAmelCase     	=       sd_pipe.to(_A	)
				sd_pipe.set_progress_bar_config(disable=_A	)

				UpperCAmelCase     	=       self.get_dummy_inputs(_A	)
				UpperCAmelCase     	=       sd_pipe(**_A	).images
				UpperCAmelCase     	=       image[0, -3:, -3:, -1]
				assert image.shape == (1, 6_4, 6_4, 3)

				UpperCAmelCase     	=       np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
	def   _lowercase       (					self	):






				'''simple docstring'''







				super().test_inference_batch_consistent(batch_sizes=[1, 2]	)
	def   _lowercase       (					self	):






				'''simple docstring'''







				super().test_inference_batch_single_identical(batch_size=2							,      expected_max_diff=3E-3	)







	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase     	=       self.get_dummy_components()
				UpperCAmelCase     	=       StableDiffusionParadigmsPipeline(**_A	)
				UpperCAmelCase     	=       sd_pipe.to(_A	)
				sd_pipe.set_progress_bar_config(disable=_A	)

				UpperCAmelCase     	=       self.get_dummy_inputs(_A	)
				UpperCAmelCase     	=       '''french fries'''
				UpperCAmelCase     	=       sd_pipe(**_A							,      negative_prompt=_A	)
				UpperCAmelCase     	=       output.images
				UpperCAmelCase     	=       image[0, -3:, -3:, -1]

				assert image.shape == (1, 6_4, 6_4, 3)

				UpperCAmelCase     	=       np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]	)

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

@slow
@require_torch_gpu
class 			A_ (unittest.TestCase    ):
	def   _lowercase       (					self	):






				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
	def   _lowercase       (					self							,      _A=0	):






				'''simple docstring'''







				UpperCAmelCase     	=       torch.Generator(device=_A	).manual_seed(_A	)
				UpperCAmelCase     	=       {
				    '''prompt''': '''a photograph of an astronaut riding a horse''',
				    '''generator''': generator,
				    '''num_inference_steps''': 1_0,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				    '''parallel''': 3,
				    '''debug''': True,
				}
				return inputs







	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''stabilityai/stable-diffusion-2-base'''
				UpperCAmelCase     	=       DDIMParallelScheduler.from_pretrained(_A							,      subfolder='''scheduler'''	)
				UpperCAmelCase     	=       StableDiffusionParadigmsPipeline.from_pretrained(_A							,      scheduler=_A							,      safety_checker=_A	)
				pipe.to(_A	)
				pipe.set_progress_bar_config(disable=_A	)
				pipe.enable_attention_slicing()

				UpperCAmelCase     	=       self.get_inputs()
				UpperCAmelCase     	=       pipe(**_A	).images
				UpperCAmelCase     	=       image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_1_2, 5_1_2, 3)

				UpperCAmelCase     	=       np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]	)

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










import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase     	=       TaConfig.from_pretrained(UpperCamelCase__				)
			UpperCAmelCase     	=       FlaxTaForConditionalGeneration(config=UpperCamelCase__				)
			UpperCAmelCase     	=       checkpoints.load_tax_checkpoint(UpperCamelCase__				)

			UpperCAmelCase     	=       '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

			# Encoder
			for layer_index in range(config.num_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

						# Self-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_attention_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo
						UpperCAmelCase     	=       tax_mlp_layer_norm

			# Only for layer 0:
			UpperCAmelCase     	=       tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
			UpperCAmelCase     	=       tax_encoder_rel_embedding

			# Assigning
			UpperCAmelCase     	=       tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
			UpperCAmelCase     	=       tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_decoder_layers				):
						UpperCAmelCase     	=       F"""layers_{str(UpperCamelCase__				)}"""

						# Self-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
						    '''scale'''
						]

						# Encoder-Decoder-Attention
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
						    '''kernel'''
						]
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
						    '''kernel'''
						]

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

						# MLP
						if split_mlp_wi:
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
						else:
									UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

						# Layer Normalization
						UpperCAmelCase     	=       tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

						# Assigning
						UpperCAmelCase     	=       tax_attention_key
						UpperCAmelCase     	=       tax_attention_out
						UpperCAmelCase     	=       tax_attention_query
						UpperCAmelCase     	=       tax_attention_value

						UpperCAmelCase     	=       tax_pre_attention_layer_norm

						UpperCAmelCase     	=       tax_enc_dec_attention_key
						UpperCAmelCase     	=       tax_enc_dec_attention_out
						UpperCAmelCase     	=       tax_enc_dec_attention_query
						UpperCAmelCase     	=       tax_enc_dec_attention_value

						UpperCAmelCase     	=       tax_cross_layer_norm

						if split_mlp_wi:
									UpperCAmelCase     	=       tax_mlp_wi_a
									UpperCAmelCase     	=       tax_mlp_wi_a
						else:
									UpperCAmelCase     	=       tax_mlp_wi

						UpperCAmelCase     	=       tax_mlp_wo

						UpperCAmelCase     	=       txa_mlp_layer_norm

			# Decoder Normalization
			UpperCAmelCase     	=       tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
			UpperCAmelCase     	=       txa_decoder_norm

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

			# Token Embeddings
			UpperCAmelCase     	=       tax_model['''target''']['''token_embedder''']['''embedding''']
			UpperCAmelCase     	=       txa_token_embeddings

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

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


if __name__ == "__main__":
				__A       :					Union[str, Any]     					=     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
				)
				parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
				parser.add_argument(
				    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
				)
				__A       :					Any     					=     parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



