
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 				UpperCAmelCase (    _snake_case    ):




	'''simple docstring'''





	snake_case_          =	42
	snake_case_          =	None





def 		UpperCAmelCase     (      a_						,  a_=0.999						,  a_="cosine"						,  )						->      Union[str, Any]:







				"""simple docstring"""



				if alpha_transform_type == "cosine":

								def alpha_bar_fn(a_		):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(a_		):
												return math.exp(t * -1_2.0		)

				else:
								raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

				__A       =					[]
				for i in range(__UpperCAmelCase		):
								__A       =					i / num_diffusion_timesteps
								__A       =					(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase		) / alpha_bar_fn(__UpperCAmelCase		)						,  __UpperCAmelCase		)		)
				return torch.tensor(__UpperCAmelCase						,  dtype=torch.floataa		)





class 				UpperCAmelCase (    _snake_case     ,     _snake_case    ):




	'''simple docstring'''





	@register_to_config
	def __init__(			self   :					Tuple				,A   :					int = 10_00				,A   :					str = "fixed_small_log"				,A   :					bool = True				,A   :					Optional[float] = 1.0				,A   :					str = "epsilon"				,A   :					str = "squaredcos_cap_v2"				,):

					if beta_schedule != "squaredcos_cap_v2":
									raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"					)

					__A       =					betas_for_alpha_bar(A					)

					__A       =					1.0 - self.betas
					__A       =					torch.cumprod(self.alphas				,dim=0					)
					__A       =					torch.tensor(1.0					)

					# standard deviation of the initial noise distribution
					__A       =					1.0

					# setable values
					__A       =					None
					__A       =					torch.from_numpy(np.arange(0				,A					)[::-1].copy()					)

					__A       =					variance_type






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					torch.FloatTensor				,A   :					Optional[int] = None					):
					return sample






	def 							UpperCamelCase_    (			self   :					str				,A   :					int				,A   :					Union[str, torch.device] = None					):
					__A       =					num_inference_steps
					__A       =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
					__A       =					(np.arange(0				,A					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
					__A       =					torch.from_numpy(A					).to(A					)






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Dict				,A   :					Optional[Any]=None				,A   :					List[str]=None				,A   :					Any=None					):

					if prev_timestep is None:
									__A       =					t - 1

					__A       =					self.alphas_cumprod[t]
					__A       =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					__A       =					1 - alpha_prod_t
					__A       =					1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
									__A       =					self.betas[t]
					else:
									__A       =					1 - alpha_prod_t / alpha_prod_t_prev

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					__A       =					beta_prod_t_prev / beta_prod_t * beta

					if variance_type is None:
									__A       =					self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small_log":
									__A       =					torch.log(torch.clamp(A				,min=1E-20					)					)
									__A       =					torch.exp(0.5 * variance					)
					elif variance_type == "learned_range":
									# NOTE difference with DDPM scheduler
									__A       =					variance.log()
									__A       =					beta.log()

									__A       =					(predicted_variance + 1) / 2
									__A       =					frac * max_log + (1 - frac) * min_log

					return variance






	def 							UpperCamelCase_    (			self   :					str				,A   :					torch.FloatTensor				,A   :					int				,A   :					torch.FloatTensor				,A   :					Optional[int] = None				,A   :					Tuple=None				,A   :					bool = True				,):
					__A       =					timestep

					if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
									__A     ,				__A       =					torch.split(A				,sample.shape[1]				,dim=1					)
					else:
									__A       =					None

					# 1. compute alphas, betas
					if prev_timestep is None:
									__A       =					t - 1

					__A       =					self.alphas_cumprod[t]
					__A       =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
					__A       =					1 - alpha_prod_t
					__A       =					1 - alpha_prod_t_prev

					if prev_timestep == t - 1:
									__A       =					self.betas[t]
									__A       =					self.alphas[t]
					else:
									__A       =					1 - alpha_prod_t / alpha_prod_t_prev
									__A       =					1 - beta

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
									__A       =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
									__A       =					model_output
					else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
									    " for the UnCLIPScheduler."					)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
									__A       =					torch.clamp(
									    A				,-self.config.clip_sample_range				,self.config.clip_sample_range					)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					__A       =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
					__A       =					alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					__A       =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					__A       =					0
					if t > 0:
									__A       =					randn_tensor(
									    model_output.shape				,dtype=model_output.dtype				,generator=A				,device=model_output.device					)

									__A       =					self._get_variance(
									    A				,predicted_variance=A				,prev_timestep=A				,)

									if self.variance_type == "fixed_small_log":
													__A       =					variance
									elif self.variance_type == "learned_range":
													__A       =					(0.5 * variance).exp()
									else:
													raise ValueError(
													    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
													    " for the UnCLIPScheduler."					)

									__A       =					variance * variance_noise

					__A       =					pred_prev_sample + variance

					if not return_dict:
									return (pred_prev_sample,)

					return UnCLIPSchedulerOutput(prev_sample=A				,pred_original_sample=A					)







	def 							UpperCamelCase_    (			self   :					Any				,A   :					torch.FloatTensor				,A   :					torch.FloatTensor				,A   :					torch.IntTensor				,):
					__A       =					self.alphas_cumprod.to(device=original_samples.device				,dtype=original_samples.dtype					)
					__A       =					timesteps.to(original_samples.device					)

					__A       =					alphas_cumprod[timesteps] ** 0.5
					__A       =					sqrt_alpha_prod.flatten()
					while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
									__A       =					sqrt_alpha_prod.unsqueeze(-1					)

					__A       =					(1 - alphas_cumprod[timesteps]) ** 0.5
					__A       =					sqrt_one_minus_alpha_prod.flatten()
					while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
									__A       =					sqrt_one_minus_alpha_prod.unsqueeze(-1					)

					__A       =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples







'''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
a   	=    {
    "/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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							list(s_dict.keys()   )
   for key in keys:
      __SCREAMING_SNAKE_CASE   				=							R""".*/layers_(\d+)"""
      __SCREAMING_SNAKE_CASE   				=							key
      if re.match(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							re.sub(R"""layers_(\d+)"""				,						R"""block/\1/layer"""				,						__UpperCAmelCase   )

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

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

         elif groups[0] == "decoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/2/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							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:
            __SCREAMING_SNAKE_CASE   				=							new_key.replace(__UpperCAmelCase				,						__UpperCAmelCase   )

      print(f"""{key} -> {new_key}"""   )
      __SCREAMING_SNAKE_CASE   				=							s_dict.pop(__UpperCAmelCase   )

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      __SCREAMING_SNAKE_CASE   				=							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:
      __SCREAMING_SNAKE_CASE   				=							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:
         __SCREAMING_SNAKE_CASE   				=							s_dict[key].shape[0]
         __SCREAMING_SNAKE_CASE   				=							s_dict[key]
         for idx in range(__UpperCAmelCase   ):
            __SCREAMING_SNAKE_CASE   				=							expert_weihts[idx]
            print(f"""{key} -> {key.replace('expert/'				,						'nested fstring'   )}"""   )

         s_dict.pop(__UpperCAmelCase   )

   return s_dict


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   import regex as re

   with open(__UpperCAmelCase				,						"""r"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.read()

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

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

   __SCREAMING_SNAKE_CASE   				=							num_experts
   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig(**__UpperCAmelCase   )
   return config

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







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

   if gin_file is not None:
      __SCREAMING_SNAKE_CASE   				=							convert_gin_to_config(__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							flax_params["""target"""]
   __SCREAMING_SNAKE_CASE   				=							flatten_dict(__UpperCAmelCase				,						sep="""/"""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							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__":
    a   	=    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")
    a   	=    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,
    )





