





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCamelCase__   : int					  = {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}




def    A_(			A   ):
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 UpperCAmelCase_								=     list(s_dict.keys()   )
 for key in keys:
  UpperCAmelCase_								=     R""".*/layers_(\d+)"""
  UpperCAmelCase_								=     key
  if re.match(A				,      A   ):
   UpperCAmelCase_								=     re.sub(R"""layers_(\d+)"""				,      R"""block/\1/layer"""				,      A   )

  UpperCAmelCase_								=     R"""(encoder|decoder)\/"""

  if re.match(A				,      A   ):
   UpperCAmelCase_								=     re.match(A				,      A   ).groups()
   if groups[0] == "encoder":
    UpperCAmelCase_								=     re.sub(R"""/mlp/"""				,      R"""/1/mlp/"""				,      A   )
    UpperCAmelCase_								=     re.sub(R"""/pre_mlp_layer_norm/"""				,      R"""/1/layer_norm/"""				,      A   )

   elif groups[0] == "decoder":
    UpperCAmelCase_								=     re.sub(R"""/mlp/"""				,      R"""/2/mlp/"""				,      A   )
    UpperCAmelCase_								=     re.sub(R"""/pre_mlp_layer_norm/"""				,      R"""/2/layer_norm/"""				,      A   )

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    UpperCAmelCase_								=     new_key.replace(A				,      A   )

  print(f"""{key} -> {new_key}"""   )
  UpperCAmelCase_								=     s_dict.pop(A   )

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCAmelCase_								=     s_dict[
      """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
  ].T
 if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCAmelCase_								=     s_dict[
      """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
  ].T

 # 3. Take extra care of the EXPERTS layer
 for key in list(s_dict.keys()   ):
  if "expert" in key:
   UpperCAmelCase_								=     s_dict[key].shape[0]
   UpperCAmelCase_								=     s_dict[key]
   for idx in range(A   ):
    UpperCAmelCase_								=     expert_weihts[idx]
    print(f"""{key} -> {key.replace('expert/'				,      'nested fstring'   )}"""   )

   s_dict.pop(A   )

 return s_dict


UpperCamelCase__   : Optional[int]					  = {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}




def    A_(			A				,      A   ):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(A				,      """r"""   ) as f:
  UpperCAmelCase_								=     f.read()

 UpperCAmelCase_								=     re.findall(R"""(.*) = ([0-9.]*)"""				,      A   )
 UpperCAmelCase_								=     {}
 for param, value in regex_match:
  if param in GIN_TO_CONFIG_MAPPING and value != "":
   UpperCAmelCase_								=     float(A   ) if """.""" in value else int(A   )

 UpperCAmelCase_								=     re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,      A   )[0]
 UpperCAmelCase_								=     str(activation[1]   )

 UpperCAmelCase_								=     num_experts
 UpperCAmelCase_								=     SwitchTransformersConfig(**A   )
 return config




def    A_(			A				,      A				,      A=None				,      A="./"				,      A=8   ):
 # Initialise PyTorch model

 print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
 UpperCAmelCase_								=     checkpoints.load_tax_checkpoint(A   )

 if gin_file is not None:
  UpperCAmelCase_								=     convert_gin_to_config(A				,      A   )
 else:
  UpperCAmelCase_								=     SwitchTransformersConfig.from_pretrained(A   )

 UpperCAmelCase_								=     SwitchTransformersForConditionalGeneration(A   )

 UpperCAmelCase_								=     flax_params["""target"""]
 UpperCAmelCase_								=     flatten_dict(A				,      sep="""/"""   )
 UpperCAmelCase_								=     rename_keys(A   )
 UpperCAmelCase_								=     unflatten_dict(A				,      sep="""/"""   )

 # Load the flax params in the PT model
 load_flax_weights_in_pytorch_model(A				,      A   )

 print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
 pt_model.save_pretrained(A   )


if __name__ == "__main__":
    UpperCamelCase__   : Optional[Any]					  = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--switch_t5x_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
            """ model architecture. If not provided, a `gin_file` has to be provided."""
        ),
    )
    parser.add_argument(
        """--gin_file""",
        default=None,
        type=str,
        required=False,
        help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
    )
    parser.add_argument(
        """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
    )
    parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
    UpperCamelCase__   : Union[str, Any]					  = parser.parse_args()
    convert_flax_checkpoint_to_pytorch(
        args.switch_tax_checkpoint_path,
        args.config_name,
        args.gin_file,
        args.pytorch_dump_folder_path,
        args.num_experts,
    )









from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Union[List[PIL.Image.Image], np.ndarray]
					lowerCamelCase     :     Optional[List[bool]]


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_cycle_diffusion import CycleDiffusionPipeline
				from .pipeline_stable_diffusion import StableDiffusionPipeline
				from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
				from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
				from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
				from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
				from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
				from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
				from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
				from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
				from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
				from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
				from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
				from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
				from .pipeline_stable_unclip import StableUnCLIPPipeline
				from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
				from .safety_checker import StableDiffusionSafetyChecker
				from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
				from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import (
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				)
else:
				from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
				from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
				from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
				if not (
				    is_torch_available()
				    and is_transformers_available()
				    and is_k_diffusion_available()
				    and is_k_diffusion_version(""">=""", """0.0.12""")
				):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not (is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_onnx_objects import *  # noqa F403
else:
				from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
				from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
				from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
				from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
				from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
				import flax







				@flax.struct.dataclass
				class   _UpperCamelCase  (       A_  ):







									'''simple docstring'''

									lowerCamelCase     :     np.ndarray
									lowerCamelCase     :     List[bool]


				from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
				from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
				from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
				from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
				from .safety_checker_flax import FlaxStableDiffusionSafetyChecker


