

'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def       snake_case   (	snake_case      : List[Any]					,		snake_case      : Dict					,		snake_case      : Optional[Any]			) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							AutoConfig.from_pretrained(snake_case			)
    lowerCAmelCase 						=							FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case			)
    lowerCAmelCase 						=							checkpoints.load_tax_checkpoint(snake_case			)

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

    if config.model_type == "t5":
        lowerCAmelCase 						=							'SelfAttention'
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        lowerCAmelCase 						=							'LocalSelfAttention'
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        lowerCAmelCase 						=							'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			):
        lowerCAmelCase 						=							F'layers_{str(snake_case			)}'

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

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

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

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

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

        # Assigning
        lowerCAmelCase 						=							flax_model.params['encoder']['block'][str(snake_case			)]['layer']
        lowerCAmelCase 						=							tax_attention_key
        lowerCAmelCase 						=							tax_attention_out
        lowerCAmelCase 						=							tax_attention_query
        lowerCAmelCase 						=							tax_attention_value

        lowerCAmelCase 						=							tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            lowerCAmelCase 						=							tax_global_layer_norm

        if split_mlp_wi:
            lowerCAmelCase 						=							tax_mlp_wi_a
            lowerCAmelCase 						=							tax_mlp_wi_a
        else:
            lowerCAmelCase 						=							tax_mlp_wi

        lowerCAmelCase 						=							tax_mlp_wo
        lowerCAmelCase 						=							tax_mlp_layer_norm

        lowerCAmelCase 						=							flax_model_encoder_layer_block

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

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        lowerCAmelCase 						=							tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
        lowerCAmelCase 						=							tax_encoder_global_rel_embedding

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

    # Decoder
    for layer_index in range(config.num_layers			):
        lowerCAmelCase 						=							F'layers_{str(snake_case			)}'

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

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

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

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

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

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

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

        # Assigning
        lowerCAmelCase 						=							flax_model.params['decoder']['block'][str(snake_case			)]['layer']
        lowerCAmelCase 						=							tax_attention_key
        lowerCAmelCase 						=							tax_attention_out
        lowerCAmelCase 						=							tax_attention_query
        lowerCAmelCase 						=							tax_attention_value

        lowerCAmelCase 						=							tax_pre_attention_layer_norm

        lowerCAmelCase 						=							tax_enc_dec_attention_key
        lowerCAmelCase 						=							tax_enc_dec_attention_out
        lowerCAmelCase 						=							tax_enc_dec_attention_query
        lowerCAmelCase 						=							tax_enc_dec_attention_value

        lowerCAmelCase 						=							tax_cross_layer_norm

        if split_mlp_wi:
            lowerCAmelCase 						=							tax_mlp_wi_a
            lowerCAmelCase 						=							tax_mlp_wi_a
        else:
            lowerCAmelCase 						=							tax_mlp_wi

        lowerCAmelCase 						=							tax_mlp_wo

        lowerCAmelCase 						=							txa_mlp_layer_norm

        lowerCAmelCase 						=							flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
     _UpperCamelCase						:  int							       =				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."
     )
     _UpperCamelCase						:  List[Any]							       =				parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''



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 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

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

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

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

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

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

      lowerCAmelCase 						=							jax.random.PRNGKey(0	)
      lowerCAmelCase 						=							jax.random.split(_SCREAMING_SNAKE_CASE      , jax.device_count()	)

      lowerCAmelCase 						=							replicate(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							shard(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							pipe(
          prompt_ids=_SCREAMING_SNAKE_CASE      , image=_SCREAMING_SNAKE_CASE      , params=_SCREAMING_SNAKE_CASE      , prng_seed=_SCREAMING_SNAKE_CASE      , num_inference_steps=50      , jit=_SCREAMING_SNAKE_CASE      , ).images
      assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





