
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[Any]							,			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				str       ):
						lowercase       :    List[Any]											=  TaConfig.from_pretrained(UpperCAmelCase_       )
						lowercase       :    Optional[Any]											=  FlaxTaForConditionalGeneration(config=UpperCAmelCase_       )
						lowercase       :    str											=  checkpoints.load_tax_checkpoint(UpperCAmelCase_       )

						lowercase       :    Tuple											=  '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

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

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

												# Layer Normalization
												lowercase       :    List[str]											=  tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

												lowercase       :    List[str]											=  tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												lowercase       :    Optional[Any]											=  tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												lowercase       :    Dict											=  tax_attention_key
												lowercase       :    List[str]											=  tax_attention_out
												lowercase       :    Optional[int]											=  tax_attention_query
												lowercase       :    str											=  tax_attention_value

												lowercase       :    int											=  tax_attention_layer_norm

												if split_mlp_wi:
																		lowercase       :    Optional[Any]											=  tax_mlp_wi_a
																		lowercase       :    List[str]											=  tax_mlp_wi_a
												else:
																		lowercase       :    Dict											=  tax_mlp_wi

												lowercase       :    Any											=  tax_mlp_wo
												lowercase       :    Union[str, Any]											=  tax_mlp_layer_norm

						# Only for layer 0:
						lowercase       :    List[str]											=  tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						lowercase       :    Dict											=  tax_encoder_rel_embedding

						# Assigning
						lowercase       :    Optional[Any]											=  tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						lowercase       :    str											=  tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers       ):
												lowercase       :    Optional[int]											=  f'''layers_{str(UpperCAmelCase_       )}'''

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

												# Layer Normalization
												lowercase       :    List[Any]											=  tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												lowercase       :    Tuple											=  tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												lowercase       :    Union[str, Any]											=  tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												lowercase       :    Dict											=  tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												lowercase       :    Dict											=  tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												lowercase       :    List[Any]											=  tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

												lowercase       :    List[str]											=  tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												lowercase       :    Union[str, Any]											=  tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												lowercase       :    Tuple											=  tax_attention_key
												lowercase       :    Dict											=  tax_attention_out
												lowercase       :    Dict											=  tax_attention_query
												lowercase       :    Optional[int]											=  tax_attention_value

												lowercase       :    Dict											=  tax_pre_attention_layer_norm

												lowercase       :    Union[str, Any]											=  tax_enc_dec_attention_key
												lowercase       :    str											=  tax_enc_dec_attention_out
												lowercase       :    int											=  tax_enc_dec_attention_query
												lowercase       :    Union[str, Any]											=  tax_enc_dec_attention_value

												lowercase       :    Dict											=  tax_cross_layer_norm

												if split_mlp_wi:
																		lowercase       :    List[Any]											=  tax_mlp_wi_a
																		lowercase       :    Union[str, Any]											=  tax_mlp_wi_a
												else:
																		lowercase       :    int											=  tax_mlp_wi

												lowercase       :    Any											=  tax_mlp_wo

												lowercase       :    List[str]											=  txa_mlp_layer_norm

						# Decoder Normalization
						lowercase       :    List[Any]											=  tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						lowercase       :    Tuple											=  txa_decoder_norm

						# Only for layer 0:
						lowercase       :    Optional[int]											=  tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						lowercase       :    Optional[Any]											=  tax_decoder_rel_embedding

						# Token Embeddings
						lowercase       :    Any											=  tax_model['''target''']['''token_embedder''']['''embedding''']
						lowercase       :    List[Any]											=  txa_token_embeddings

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

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


if __name__ == "__main__":
				snake_case__  							=							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."""
				)
				snake_case__  							=							parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_pt_objects import *  # noqa F403
else:
				from .consistency_models import ConsistencyModelPipeline
				from .dance_diffusion import DanceDiffusionPipeline
				from .ddim import DDIMPipeline
				from .ddpm import DDPMPipeline
				from .dit import DiTPipeline
				from .latent_diffusion import LDMSuperResolutionPipeline
				from .latent_diffusion_uncond import LDMPipeline
				from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
				from .pndm import PNDMPipeline
				from .repaint import RePaintPipeline
				from .score_sde_ve import ScoreSdeVePipeline
				from .stochastic_karras_ve import KarrasVePipeline

try:
				if not (is_torch_available() and is_librosa_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
				from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
				if not (is_torch_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
				from .audioldm import AudioLDMPipeline
				from .controlnet import (
				    StableDiffusionControlNetImgaImgPipeline,
				    StableDiffusionControlNetInpaintPipeline,
				    StableDiffusionControlNetPipeline,
				)
				from .deepfloyd_if import (
				    IFImgaImgPipeline,
				    IFImgaImgSuperResolutionPipeline,
				    IFInpaintingPipeline,
				    IFInpaintingSuperResolutionPipeline,
				    IFPipeline,
				    IFSuperResolutionPipeline,
				)
				from .kandinsky import (
				    KandinskyImgaImgPipeline,
				    KandinskyInpaintPipeline,
				    KandinskyPipeline,
				    KandinskyPriorPipeline,
				)
				from .kandinskya_a import (
				    KandinskyVaaControlnetImgaImgPipeline,
				    KandinskyVaaControlnetPipeline,
				    KandinskyVaaImgaImgPipeline,
				    KandinskyVaaInpaintPipeline,
				    KandinskyVaaPipeline,
				    KandinskyVaaPriorEmbaEmbPipeline,
				    KandinskyVaaPriorPipeline,
				)
				from .latent_diffusion import LDMTextToImagePipeline
				from .paint_by_example import PaintByExamplePipeline
				from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
				from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
				from .stable_diffusion import (
				    CycleDiffusionPipeline,
				    StableDiffusionAttendAndExcitePipeline,
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionImageVariationPipeline,
				    StableDiffusionImgaImgPipeline,
				    StableDiffusionInpaintPipeline,
				    StableDiffusionInpaintPipelineLegacy,
				    StableDiffusionInstructPixaPixPipeline,
				    StableDiffusionLatentUpscalePipeline,
				    StableDiffusionLDMaDPipeline,
				    StableDiffusionModelEditingPipeline,
				    StableDiffusionPanoramaPipeline,
				    StableDiffusionParadigmsPipeline,
				    StableDiffusionPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				    StableDiffusionSAGPipeline,
				    StableDiffusionUpscalePipeline,
				    StableUnCLIPImgaImgPipeline,
				    StableUnCLIPPipeline,
				)
				from .stable_diffusion_safe import StableDiffusionPipelineSafe
				from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
				from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
				from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
				from .versatile_diffusion import (
				    VersatileDiffusionDualGuidedPipeline,
				    VersatileDiffusionImageVariationPipeline,
				    VersatileDiffusionPipeline,
				    VersatileDiffusionTextToImagePipeline,
				)
				from .vq_diffusion import VQDiffusionPipeline


try:
				if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
				from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
				if not is_onnx_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_onnx_objects import *  # noqa F403
else:
				from .onnx_utils import OnnxRuntimeModel

try:
				if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
				from .stable_diffusion import (
				    OnnxStableDiffusionImgaImgPipeline,
				    OnnxStableDiffusionInpaintPipeline,
				    OnnxStableDiffusionInpaintPipelineLegacy,
				    OnnxStableDiffusionPipeline,
				    OnnxStableDiffusionUpscalePipeline,
				    StableDiffusionOnnxPipeline,
				)

try:
				if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_objects import *  # noqa F403
else:
				from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
				if not (is_flax_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
				from .controlnet import FlaxStableDiffusionControlNetPipeline
				from .stable_diffusion import (
				    FlaxStableDiffusionImgaImgPipeline,
				    FlaxStableDiffusionInpaintPipeline,
				    FlaxStableDiffusionPipeline,
				)
try:
				if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
				from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline


