

'''simple docstring'''

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
lowercase__         =      {
    "/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 				UpperCamelCase(					UpperCAmelCase_     ):
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 UpperCAmelCase	:     List[Any]						 =			list(s_dict.keys()     )
 for key in keys:
  UpperCAmelCase	:     Union[str, Any]						 =			R'.*/layers_(\d+)'
  UpperCAmelCase	:     List[str]						 =			key
  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'layers_(\d+)'  ,	R'block/\1/layer'  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     str						 =			R'(encoder|decoder)\/'

  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Tuple						 =			re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ).groups()
   if groups[0] == "encoder":
    UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'/mlp/'  ,	R'/1/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     List[Any]						 =			re.sub(R'/pre_mlp_layer_norm/'  ,	R'/1/layer_norm/'  ,	UpperCAmelCase_     )

   elif groups[0] == "decoder":
    UpperCAmelCase	:     Tuple						 =			re.sub(R'/mlp/'  ,	R'/2/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'/pre_mlp_layer_norm/'  ,	R'/2/layer_norm/'  ,	UpperCAmelCase_     )

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    UpperCAmelCase	:     List[str]						 =			new_key.replace(UpperCAmelCase_  ,	UpperCAmelCase_     )

  print(F"""{key} -> {new_key}"""     )
  UpperCAmelCase	:     List[Any]						 =			s_dict.pop(UpperCAmelCase_     )

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCAmelCase	:     Optional[int]						 =			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	:     Optional[int]						 =			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	:     List[str]						 =			s_dict[key].shape[0]
   UpperCAmelCase	:     List[Any]						 =			s_dict[key]
   for idx in range(UpperCAmelCase_     ):
    UpperCAmelCase	:     Union[str, Any]						 =			expert_weihts[idx]
    print(F"""{key} -> {key.replace("expert/"  ,	"nested fstring"     )}"""     )

   s_dict.pop(UpperCAmelCase_     )

 return s_dict


lowercase__         =      {
    "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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(UpperCAmelCase_  ,	'r'     ) as f:
  UpperCAmelCase	:     Union[str, Any]						 =			f.read()

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

 UpperCAmelCase	:     str						 =			re.findall(R'(.*activations) = \(\'(.*)\',\)'  ,	UpperCAmelCase_     )[0]
 UpperCAmelCase	:     Union[str, Any]						 =			str(activation[1]     )

 UpperCAmelCase	:     Optional[int]						 =			num_experts
 UpperCAmelCase	:     List[str]						 =			SwitchTransformersConfig(**UpperCAmelCase_     )
 return config




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_="./"  ,	UpperCAmelCase_=8     ):
 # Initialise PyTorch model

 print(F"""Loading flax weights from : {flax_checkpoint_path}"""     )
 UpperCAmelCase	:     List[Any]						 =			checkpoints.load_tax_checkpoint(UpperCAmelCase_     )

 if gin_file is not None:
  UpperCAmelCase	:     List[Any]						 =			convert_gin_to_config(UpperCAmelCase_  ,	UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     str						 =			SwitchTransformersConfig.from_pretrained(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			SwitchTransformersForConditionalGeneration(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			flax_params['target']
 UpperCAmelCase	:     Union[str, Any]						 =			flatten_dict(UpperCAmelCase_  ,	sep='/'     )
 UpperCAmelCase	:     Tuple						 =			rename_keys(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			unflatten_dict(UpperCAmelCase_  ,	sep='/'     )

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

 print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
 pt_model.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
  lowercase__         =      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")
  lowercase__         =      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,
  )







'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			num_latents
      UpperCAmelCase	:     List[Any]						 =			d_latents
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Dict						 =			num_blocks
      UpperCAmelCase	:     Optional[int]						 =			num_self_attends_per_block
      UpperCAmelCase	:     Optional[Any]						 =			num_self_attention_heads
      UpperCAmelCase	:     Optional[Any]						 =			num_cross_attention_heads
      UpperCAmelCase	:     Tuple						 =			qk_channels
      UpperCAmelCase	:     Optional[int]						 =			v_channels
      UpperCAmelCase	:     str						 =			cross_attention_shape_for_attention
      UpperCAmelCase	:     Union[str, Any]						 =			self_attention_widening_factor
      UpperCAmelCase	:     List[Any]						 =			cross_attention_widening_factor
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			layer_norm_eps
      UpperCAmelCase	:     Any						 =			use_query_residual
      # masked language modeling attributes
      UpperCAmelCase	:     Any						 =			vocab_size
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      # image classification attributes
      UpperCAmelCase	:     str						 =			image_size
      # flow attributes
      UpperCAmelCase	:     Any						 =			train_size
      # multimodal autoencoding attributes
      UpperCAmelCase	:     Any						 =			num_frames
      UpperCAmelCase	:     List[Any]						 =			audio_samples_per_frame
      UpperCAmelCase	:     Tuple						 =			samples_per_patch
      UpperCAmelCase	:     Union[str, Any]						 =			output_shape




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_					,      lowercase_	):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch					,      num_token_to_add=0	)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase	:     int						 =			preprocessor.num_special_tokens_to_add(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=lowercase_	)
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase	:     List[Any]						 =			[' '.join(['a']	) * seq_length] * batch_size
       UpperCAmelCase	:     Union[str, Any]						 =			dict(preprocessor(lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Union[str, Any]						 =			inputs.pop('input_ids'	)
       return inputs
      elif isinstance(lowercase_					,      lowercase_	) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch	)
       UpperCAmelCase	:     Any						 =			self._generate_dummy_images(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)
       UpperCAmelCase	:     Tuple						 =			dict(preprocessor(images=lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Dict						 =			inputs.pop('pixel_values'	)
       return inputs
      else:
       raise ValueError(
           'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)




