





'''simple docstring'''

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 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]		,			_lowerCamelCase   :   Tuple="attention"			):
    A__							=     params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
    A__							=     params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
    A__							=     params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
    A__							=     params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
    return k, o, q, v





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]=False			):
    if split_mlp_wi:
        A__							=     params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
        A__							=     params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
        A__							=     (wi_a, wi_a)
    else:
        A__							=     params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

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





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   int			):
    return params[F"{prefix}/layers_{i}/{layer_name}/scale"]





def 		UpperCamelCase							(				_lowerCamelCase   :   dict		,			*, _lowerCamelCase   :   int		,			_lowerCamelCase   :   bool			):
    A__							=     traverse_util.flatten_dict(variables["target"]			)
    A__							=     {"/".join(_lowerCamelCase			): v for k, v in old.items()}

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

    A__							=     collections.OrderedDict()

    # Shared embeddings.
    A__							=     old["token_embedder/embedding"]

    # Encoder.
    for i in range(_lowerCamelCase			):
        # Block i, layer 0 (Self Attention).
        A__							=     tax_layer_norm_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"encoder"		,			"pre_attention_layer_norm"			)
        A__,      A__,      A__,      A__							=     tax_attention_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"encoder"		,			"attention"			)
        A__							=     layer_norm
        A__							=     k.T
        A__							=     o.T
        A__							=     q.T
        A__							=     v.T

        # Block i, layer 1 (MLP).
        A__							=     tax_layer_norm_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"encoder"		,			"pre_mlp_layer_norm"			)
        A__,      A__							=     tax_mlp_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"encoder"		,			_lowerCamelCase			)
        A__							=     layer_norm
        if split_mlp_wi:
            A__							=     wi[0].T
            A__							=     wi[1].T
        else:
            A__							=     wi.T
        A__							=     wo.T

    A__							=     old[
        "encoder/relpos_bias/rel_embedding"
    ].T
    A__							=     old["encoder/encoder_norm/scale"]

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

            # Block i, layer 1 (Cross Attention).
            A__							=     tax_layer_norm_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"decoder"		,			"pre_cross_attention_layer_norm"			)
            A__,      A__,      A__,      A__							=     tax_attention_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"decoder"		,			"encoder_decoder_attention"			)
            A__							=     layer_norm
            A__							=     k.T
            A__							=     o.T
            A__							=     q.T
            A__							=     v.T

            # Block i, layer 2 (MLP).
            A__							=     tax_layer_norm_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"decoder"		,			"pre_mlp_layer_norm"			)
            A__,      A__							=     tax_mlp_lookup(_lowerCamelCase		,			_lowerCamelCase		,			"decoder"		,			_lowerCamelCase			)
            A__							=     layer_norm
            if split_mlp_wi:
                A__							=     wi[0].T
                A__							=     wi[1].T
            else:
                A__							=     wi.T
            A__							=     wo.T

        A__							=     old["decoder/decoder_norm/scale"]
        A__							=     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:
            A__							=     old["decoder/logits_dense/kernel"].T

    return new





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   bool			):
    A__							=     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:
        A__							=     state_dict["shared.weight"]

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

    return state_dict





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   List[str]		,			_lowerCamelCase   :   int			):
    A__							=     checkpoints.load_tax_checkpoint(_lowerCamelCase			)
    A__							=     convert_tax_to_pytorch(_lowerCamelCase		,			num_layers=config.num_layers		,			is_encoder_only=_lowerCamelCase			)
    A__							=     make_state_dict(_lowerCamelCase		,			_lowerCamelCase			)
    model.load_state_dict(_lowerCamelCase		,			strict=_lowerCamelCase			)





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   List[str]		,			_lowerCamelCase   :   bool = False			):
    A__							=     TaConfig.from_json_file(_lowerCamelCase			)
    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:
        A__							=     TaEncoderModel(_lowerCamelCase			)
    else:
        A__							=     TaForConditionalGeneration(_lowerCamelCase			)

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

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

    # Verify that we can load the checkpoint.
    model.from_pretrained(_lowerCamelCase			)
    print("Done"			)


if __name__ == "__main__":
  __lowerCAmelCase				:      List[str]						      =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
  )
  __lowerCAmelCase				:      Union[str, Any]						      =parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
  from .pipeline_kandinsky import KandinskyPipeline
  from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
  from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
  from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
  from .text_encoder import MultilingualCLIP



