






'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[str]:
  UpperCamelCase__				:					List[Any]					=					AutoConfig.from_pretrained(lowerCamelCase_)
  UpperCamelCase__				:					Tuple					=					FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCamelCase_)
  UpperCamelCase__				:					Union[str, Any]					=					checkpoints.load_tax_checkpoint(lowerCamelCase_)

  UpperCamelCase__				:					str					=					'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

  if config.model_type == "t5":
    UpperCamelCase__				:					Union[str, Any]					=					'SelfAttention'
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    UpperCamelCase__				:					Dict					=					'LocalSelfAttention'
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    UpperCamelCase__				:					Dict					=					'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):
    UpperCamelCase__				:					int					=					f'layers_{str(lowerCamelCase_)}'

    # Self-Attention
    UpperCamelCase__				:					Any					=					tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
    UpperCamelCase__				:					Optional[int]					=					tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
    UpperCamelCase__				:					Optional[int]					=					tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
    UpperCamelCase__				:					Optional[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":
      UpperCamelCase__				:					Optional[int]					=					tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

    # Layer Normalization
    UpperCamelCase__				:					int					=					tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

    if split_mlp_wi:
      UpperCamelCase__				:					int					=					tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
      UpperCamelCase__				:					Dict					=					tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      UpperCamelCase__				:					Union[str, Any]					=					tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

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

    # Layer Normalization
    UpperCamelCase__				:					List[Any]					=					tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    UpperCamelCase__				:					List[Any]					=					flax_model.params['encoder']['block'][str(lowerCamelCase_)]['layer']
    UpperCamelCase__				:					Tuple					=					tax_attention_key
    UpperCamelCase__				:					Optional[Any]					=					tax_attention_out
    UpperCamelCase__				:					Tuple					=					tax_attention_query
    UpperCamelCase__				:					Union[str, Any]					=					tax_attention_value

    UpperCamelCase__				:					str					=					tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      UpperCamelCase__				:					Union[str, Any]					=					tax_global_layer_norm

    if split_mlp_wi:
      UpperCamelCase__				:					int					=					tax_mlp_wi_a
      UpperCamelCase__				:					int					=					tax_mlp_wi_a
    else:
      UpperCamelCase__				:					str					=					tax_mlp_wi

    UpperCamelCase__				:					Tuple					=					tax_mlp_wo
    UpperCamelCase__				:					Union[str, Any]					=					tax_mlp_layer_norm

    UpperCamelCase__				:					Any					=					flax_model_encoder_layer_block

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

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    UpperCamelCase__				:					Union[str, Any]					=					tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
    UpperCamelCase__				:					Optional[int]					=					tax_encoder_global_rel_embedding

  # Assigning
  UpperCamelCase__				:					Dict					=					tax_model['target']['encoder']['encoder_norm']['scale']
  UpperCamelCase__				:					str					=					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers):
    UpperCamelCase__				:					List[Any]					=					f'layers_{str(lowerCamelCase_)}'

    # Self-Attention
    UpperCamelCase__				:					int					=					tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
    UpperCamelCase__				:					Optional[int]					=					tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
    UpperCamelCase__				:					Any					=					tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
    UpperCamelCase__				:					Tuple					=					tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

    # Layer Normalization
    UpperCamelCase__				:					Optional[Any]					=					tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
        'scale'
    ]

    # Encoder-Decoder-Attention
    UpperCamelCase__				:					Optional[Any]					=					tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
    UpperCamelCase__				:					List[Any]					=					tax_enc_dec_attention_module['key']['kernel']
    UpperCamelCase__				:					str					=					tax_enc_dec_attention_module['out']['kernel']
    UpperCamelCase__				:					List[Any]					=					tax_enc_dec_attention_module['query']['kernel']
    UpperCamelCase__				:					Union[str, Any]					=					tax_enc_dec_attention_module['value']['kernel']

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

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

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

    # Layer Normalization
    UpperCamelCase__				:					Any					=					tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

    # Assigning
    UpperCamelCase__				:					List[str]					=					flax_model.params['decoder']['block'][str(lowerCamelCase_)]['layer']
    UpperCamelCase__				:					List[Any]					=					tax_attention_key
    UpperCamelCase__				:					int					=					tax_attention_out
    UpperCamelCase__				:					Tuple					=					tax_attention_query
    UpperCamelCase__				:					Dict					=					tax_attention_value

    UpperCamelCase__				:					int					=					tax_pre_attention_layer_norm

    UpperCamelCase__				:					Optional[int]					=					tax_enc_dec_attention_key
    UpperCamelCase__				:					List[Any]					=					tax_enc_dec_attention_out
    UpperCamelCase__				:					int					=					tax_enc_dec_attention_query
    UpperCamelCase__				:					Dict					=					tax_enc_dec_attention_value

    UpperCamelCase__				:					Tuple					=					tax_cross_layer_norm

    if split_mlp_wi:
      UpperCamelCase__				:					int					=					tax_mlp_wi_a
      UpperCamelCase__				:					Dict					=					tax_mlp_wi_a
    else:
      UpperCamelCase__				:					int					=					tax_mlp_wi

    UpperCamelCase__				:					Any					=					tax_mlp_wo

    UpperCamelCase__				:					Any					=					txa_mlp_layer_norm

    UpperCamelCase__				:					Tuple					=					flax_model_decoder_layer_block

  # Decoder Normalization
  UpperCamelCase__				:					Optional[int]					=					tax_model['target']['decoder']['decoder_norm']['scale']
  UpperCamelCase__				:					Optional[int]					=					txa_decoder_norm

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

  # Token Embeddings
  UpperCamelCase__				:					Dict					=					tax_model['target']['token_embedder']['embedding']
  UpperCamelCase__				:					int					=					txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    UpperCamelCase__				:					Union[str, Any]					=					tax_model['target']['decoder']['logits_dense']['kernel']

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


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












'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class       __lowercase					:

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			torch.manual_seed(0)
			UpperCamelCase__				:					Dict					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Union[str, Any]					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=1  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=3  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Dict):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5')

			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					UNetaDConditionModel(
			    sample_size=32  ,					layers_per_block=[1, 2]  ,					block_out_channels=[32, 64]  ,					down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]  ,					mid_block_type='UNetMidBlock2DSimpleCrossAttn'  ,					up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']  ,					in_channels=6  ,					out_channels=6  ,					cross_attention_dim=32  ,					encoder_hid_dim=32  ,					attention_head_dim=8  ,					addition_embed_type='text'  ,					addition_embed_type_num_heads=2  ,					cross_attention_norm='group_norm'  ,					resnet_time_scale_shift='scale_shift'  ,					act_fn='gelu'  ,					class_embed_type='timestep'  ,					mid_block_scale_factor=1.4_14  ,					time_embedding_act_fn='gelu'  ,					time_embedding_dim=32  ,					)
			unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			torch.manual_seed(0)
			UpperCamelCase__				:					str					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					thresholding=UpperCAmelCase_  ,					dynamic_thresholding_ratio=0.95  ,					sample_max_value=1.0  ,					prediction_type='epsilon'  ,					variance_type='learned_range'  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					DDPMScheduler(
			    num_train_timesteps=1_000  ,					beta_schedule='squaredcos_cap_v2'  ,					beta_start=0.00_01  ,					beta_end=0.02  ,					)

			torch.manual_seed(0)
			UpperCamelCase__				:					Optional[Any]					=					IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "image_noising_scheduler": image_noising_scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Dict					=					self.get_dummy_components()
			UpperCamelCase__				:					List[Any]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					inputs['prompt']
			UpperCamelCase__				:					List[Any]					=					inputs['generator']
			UpperCamelCase__				:					Tuple					=					inputs['num_inference_steps']
			UpperCamelCase__				:					List[Any]					=					inputs['output_type']

			if "image" in inputs:
					UpperCamelCase__				:					Tuple					=					inputs['image']
			else:
					UpperCamelCase__				:					Union[str, Any]					=					None

			if "mask_image" in inputs:
					UpperCamelCase__				:					Optional[int]					=					inputs['mask_image']
			else:
					UpperCamelCase__				:					int					=					None

			if "original_image" in inputs:
					UpperCamelCase__				:					List[Any]					=					inputs['original_image']
			else:
					UpperCamelCase__				:					Optional[Any]					=					None

			UpperCamelCase__,	UpperCamelCase__				:					Any					=					pipe.encode_prompt(UpperCAmelCase_)

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					List[Any]					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Dict					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Optional[int]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					original_image

			# set all optional components to None
			for optional_component in pipe._optional_components:
					setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

			UpperCamelCase__				:					int					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(UpperCAmelCase_  ,					UpperCAmelCase_) is None  ,					F'`{optional_component}` did not stay set to None after loading.'  ,					)

			UpperCamelCase__				:					Optional[int]					=					self.get_dummy_inputs(UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					inputs['generator']
			UpperCamelCase__				:					List[Any]					=					inputs['num_inference_steps']
			UpperCamelCase__				:					Optional[int]					=					inputs['output_type']

			# inputs with prompt converted to embeddings
			UpperCamelCase__				:					Any					=					{
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					UpperCamelCase__				:					Tuple					=					image

			if mask_image is not None:
					UpperCamelCase__				:					Union[str, Any]					=					mask_image

			if original_image is not None:
					UpperCamelCase__				:					str					=					original_image

			UpperCamelCase__				:					Union[str, Any]					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Dict					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)





	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Any					=					self.get_dummy_components()
			UpperCamelCase__				:					List[str]					=					self.pipeline_class(**UpperCAmelCase_)
			pipe.to(UpperCAmelCase_)
			pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					Union[str, Any]					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					pipe(**UpperCAmelCase_)[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(UpperCAmelCase_)
					UpperCamelCase__				:					Optional[Any]					=					self.pipeline_class.from_pretrained(UpperCAmelCase_)
					pipe_loaded.to(UpperCAmelCase_)
					pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_)

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor())  # For reproducibility tests

			UpperCamelCase__				:					Any					=					self.get_dummy_inputs(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					pipe_loaded(**UpperCAmelCase_)[0]

			UpperCamelCase__				:					Optional[int]					=					np.abs(to_np(UpperCAmelCase_) - to_np(UpperCAmelCase_)).max()
			self.assertLess(UpperCAmelCase_  ,					1e-4)




