





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 lowerCamelCase__   (							snake_case_     :  List[Any]	,				snake_case_     :  int	,				snake_case_     :  List[Any]	,				snake_case_     :  Optional[int]="attention"	)				->    Tuple:
   __snake_case								=       params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
   __snake_case								=       params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
   __snake_case								=       params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
   __snake_case								=       params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
   return k, o, q, v






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

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






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  List[Any]	,				snake_case_     :  List[Any]	,				snake_case_     :  int	)				->    Dict:
   return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]






def lowerCamelCase__   (							snake_case_     :  dict	,				*, snake_case_     :  int	,				snake_case_     :  bool	)				->    Optional[Any]:
   __snake_case								=       traverse_util.flatten_dict(variables['''target''']	)
   __snake_case								=       {'''/'''.join(snake_case_	): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   __snake_case								=       '''encoder/layers_0/mlp/wi_0/kernel''' in old
   print('''Split MLP:'''	,				snake_case_	)

   __snake_case								=       collections.OrderedDict()

   # Shared embeddings.
   __snake_case								=       old['''token_embedder/embedding''']

   # Encoder.
   for i in range(snake_case_	):
      # Block i, layer 0 (Self Attention).
      __snake_case								=       tax_layer_norm_lookup(snake_case_	,				snake_case_	,				'''encoder'''	,				'''pre_attention_layer_norm'''	)
      __snake_case	,     __snake_case	,     __snake_case	,     __snake_case								=       tax_attention_lookup(snake_case_	,				snake_case_	,				'''encoder'''	,				'''attention'''	)
      __snake_case								=       layer_norm
      __snake_case								=       k.T
      __snake_case								=       o.T
      __snake_case								=       q.T
      __snake_case								=       v.T

      # Block i, layer 1 (MLP).
      __snake_case								=       tax_layer_norm_lookup(snake_case_	,				snake_case_	,				'''encoder'''	,				'''pre_mlp_layer_norm'''	)
      __snake_case	,     __snake_case								=       tax_mlp_lookup(snake_case_	,				snake_case_	,				'''encoder'''	,				snake_case_	)
      __snake_case								=       layer_norm
      if split_mlp_wi:
         __snake_case								=       wi[0].T
         __snake_case								=       wi[1].T
      else:
         __snake_case								=       wi.T
      __snake_case								=       wo.T

   __snake_case								=       old[
       '''encoder/relpos_bias/rel_embedding'''
   ].T
   __snake_case								=       old['''encoder/encoder_norm/scale''']

   if not is_encoder_only:
      # Decoder.
      for i in range(snake_case_	):
         # Block i, layer 0 (Self Attention).
         __snake_case								=       tax_layer_norm_lookup(snake_case_	,				snake_case_	,				'''decoder'''	,				'''pre_self_attention_layer_norm'''	)
         __snake_case	,     __snake_case	,     __snake_case	,     __snake_case								=       tax_attention_lookup(snake_case_	,				snake_case_	,				'''decoder'''	,				'''self_attention'''	)
         __snake_case								=       layer_norm
         __snake_case								=       k.T
         __snake_case								=       o.T
         __snake_case								=       q.T
         __snake_case								=       v.T

         # Block i, layer 1 (Cross Attention).
         __snake_case								=       tax_layer_norm_lookup(snake_case_	,				snake_case_	,				'''decoder'''	,				'''pre_cross_attention_layer_norm'''	)
         __snake_case	,     __snake_case	,     __snake_case	,     __snake_case								=       tax_attention_lookup(snake_case_	,				snake_case_	,				'''decoder'''	,				'''encoder_decoder_attention'''	)
         __snake_case								=       layer_norm
         __snake_case								=       k.T
         __snake_case								=       o.T
         __snake_case								=       q.T
         __snake_case								=       v.T

         # Block i, layer 2 (MLP).
         __snake_case								=       tax_layer_norm_lookup(snake_case_	,				snake_case_	,				'''decoder'''	,				'''pre_mlp_layer_norm'''	)
         __snake_case	,     __snake_case								=       tax_mlp_lookup(snake_case_	,				snake_case_	,				'''decoder'''	,				snake_case_	)
         __snake_case								=       layer_norm
         if split_mlp_wi:
            __snake_case								=       wi[0].T
            __snake_case								=       wi[1].T
         else:
            __snake_case								=       wi.T
         __snake_case								=       wo.T

      __snake_case								=       old['''decoder/decoder_norm/scale''']
      __snake_case								=       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:
         __snake_case								=       old['''decoder/logits_dense/kernel'''].T

   return new






def lowerCamelCase__   (							snake_case_     :  Tuple	,				snake_case_     :  bool	)				->    str:
   __snake_case								=       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:
      __snake_case								=       state_dict['''shared.weight''']

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         __snake_case								=       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.'''	)
         __snake_case								=       state_dict['''shared.weight''']

   return state_dict






def lowerCamelCase__   (							snake_case_     :  Dict	,				snake_case_     :  List[Any]	,				snake_case_     :  Optional[int]	,				snake_case_     :  Any	)				->    Dict:
   __snake_case								=       checkpoints.load_tax_checkpoint(snake_case_	)
   __snake_case								=       convert_tax_to_pytorch(snake_case_	,				num_layers=config.num_layers	,				is_encoder_only=snake_case_	)
   __snake_case								=       make_state_dict(snake_case_	,				snake_case_	)
   model.load_state_dict(snake_case_	,				strict=snake_case_	)






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  Union[str, Any]	,				snake_case_     :  Any	,				snake_case_     :  bool = False	)				->    Dict:
   __snake_case								=       TaConfig.from_json_file(snake_case_	)
   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:
      __snake_case								=       TaEncoderModel(snake_case_	)
   else:
      __snake_case								=       TaForConditionalGeneration(snake_case_	)

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

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

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


if __name__ == "__main__":
    snake_case_           =      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
    )
    snake_case_           =      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 unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard

@slow
@require_flax
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int				):





         """simple docstring"""



         super().tearDown()
         gc.collect()


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

         __snake_case								=       '''bird'''
         __snake_case								=       jax.device_count()
         __snake_case								=       pipe.prepare_text_inputs([prompts] * num_samples				)

         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''				)
         __snake_case								=       pipe.prepare_image_inputs([canny_image] * num_samples				)

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

         __snake_case								=       '''Chef in the kitchen'''
         __snake_case								=       jax.device_count()
         __snake_case								=       pipe.prepare_text_inputs([prompts] * num_samples				)

         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''				)
         __snake_case								=       pipe.prepare_image_inputs([pose_image] * num_samples				)

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

