


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				lowercase__						=				flax_key_tuple[:-1] + ('''weight''',)
				lowercase__						=				torch.permute(SCREAMING_SNAKE_CASE						,    (0, 2, 1)					)
		elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE					):
				# linear layer
				lowercase__						=				flax_key_tuple[:-1] + ('''weight''',)
				lowercase__						=				flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				lowercase__						=				flax_key_tuple[:-1] + ('''weight''',)

		return flax_key_tuple, flax_tensor
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if "metadata" in layer:
				lowercase__						=				layer.split('''metadata'''					)
				lowercase__						=				''''''.join(split_layer[0]					)[:-1]
				lowercase__						=				[tuple(('''metadata''' + split_layer[1]).split('''/'''					)					)]
		elif "kvstore" in layer:
				lowercase__						=				layer.split('''kvstore'''					)
				lowercase__						=				''''''.join(split_layer[0]					)[:-1]
				lowercase__						=				[tuple(('''kvstore''' + split_layer[1]).split('''/'''					)					)]

		else:
				lowercase__						=				layer.split('''/'''					)
				lowercase__						=				'''/'''.join(split_layer[:-1]					)
				lowercase__						=				(split_layer[-1],)

		if "kvstore/path" in layer:
				lowercase__						=				f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
		elif "kvstore/driver" in layer:
				lowercase__						=				'''file'''
		else:
				lowercase__						=				checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				rename_keys(SCREAMING_SNAKE_CASE					)
		lowercase__						=				{}
		for k, v in current_block.items():
				lowercase__						=				v
		lowercase__						=				new_current_block
		torch.save(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = WEIGHTS_NAME					):


		"""simple docstring"""




		lowercase__						=				convert_file_size_to_int(SCREAMING_SNAKE_CASE					)
		lowercase__						=				[]
		lowercase__						=				{}
		lowercase__						=				0
		lowercase__						=				0

		os.makedirs(SCREAMING_SNAKE_CASE						,    exist_ok=SCREAMING_SNAKE_CASE					)
		with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''						,    '''rb'''					) as fp:
				lowercase__						=				serialization.msgpack_restore(fp.read()					)['''optimizer''']['''target''']
				lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE						,    sep='''/'''					)

		lowercase__						=				{}
		for layer in checkpoint_info.keys():
				lowercase__					,		lowercase__					,		lowercase__						=				get_key_and_tensorstore_dict(
				    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
				if curr_real_layer_name in all_layers:
						lowercase__						=				content
				else:
						lowercase__						=				{split_layer[-1]: content}

		for key in all_layers.keys():
				# open tensorstore file
				lowercase__						=				ts.open(unflatten_dict(all_layers[key]					)					).result().read().result()
				lowercase__						=				torch.tensor(SCREAMING_SNAKE_CASE					)
				lowercase__						=				raw_weights.numel() * dtype_byte_size(raw_weights.dtype					)

				# use the renaming pattern from the small conversion scripts
				lowercase__					,		lowercase__						=				rename_base_flax_keys(tuple(key.split('''/'''					)					)						,    SCREAMING_SNAKE_CASE					)
				lowercase__						=				'''/'''.join(SCREAMING_SNAKE_CASE					)

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						lowercase__						=				os.path.join(
						    SCREAMING_SNAKE_CASE						,    weights_name.replace('''.bin'''						,    f'-{len(SCREAMING_SNAKE_CASE					)+1:05d}-of-???.bin'					)					)
						rename_and_save_block(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
						sharded_state_dicts.append(current_block.keys()					)
						del current_block
						lowercase__						=				{}
						lowercase__						=				0

				lowercase__						=				raw_weights.to(getattr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)					)
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		lowercase__						=				os.path.join(SCREAMING_SNAKE_CASE						,    weights_name.replace('''.bin'''						,    f'-{len(SCREAMING_SNAKE_CASE					)+1:05d}-of-???.bin'					)					)
		rename_and_save_block(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		sharded_state_dicts.append(current_block.keys()					)

		# If we only have one shard, we return it
		if len(SCREAMING_SNAKE_CASE					) == 1:
				return {weights_name: sharded_state_dicts[0]}, None

		# Otherwise, let's build the index
		lowercase__						=				{}
		lowercase__						=				{}
		for idx, shard in enumerate(SCREAMING_SNAKE_CASE					):
				lowercase__						=				weights_name.replace(
				    '''.bin'''						,    f'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE					):05d}.bin'					)  # len(sharded_state_dicts):05d}
				lowercase__						=				os.path.join(SCREAMING_SNAKE_CASE						,    weights_name.replace('''.bin'''						,    f'-{idx+1:05d}-of-???.bin'					)					)
				os.rename(SCREAMING_SNAKE_CASE						,    os.path.join(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)					)
				lowercase__						=				shard
				for key in shard:
						lowercase__						=				shard_file

    # Add the metadata
		lowercase__						=				{'''total_size''': total_size}
		lowercase__						=				{'''metadata''': metadata, '''weight_map''': weight_map}

		with open(os.path.join(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)						,    '''w'''						,    encoding='''utf-8'''					) as f:
				lowercase__						=				json.dumps(SCREAMING_SNAKE_CASE						,    indent=2						,    sort_keys=SCREAMING_SNAKE_CASE					) + '''\n'''
				f.write(SCREAMING_SNAKE_CASE					)

		return metadata, index


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--switch_t5x_checkpoint_path',
			    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
			    type=str,
			    required=False,
			    help='Path to a directory containing a folder per layer. Follows the original Google format.',
			)
			parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
			parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
			    type=str,
			    required=False,
			    help='Path to the output pytorch model.',
			)
			lowerCAmelCase	=							parser.parse_args()
			shard_on_the_fly(
			    args.switch_tax_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.max_shard_size,
			    args.dtype,
			)
def       _a			(				):


		"""simple docstring"""




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

		lowercase__						=				SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''					)
		config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''					)
		lowercase__						=				SwitchTransformersForConditionalGeneration.from_pretrained(
		    '''/home/arthur_huggingface_co/transformers/switch_converted'''						,    device_map='''auto'''					)

		lowercase__						=				TaTokenizer.from_pretrained('''t5-small'''					)
		lowercase__						=				'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

		lowercase__						=				tokenizer(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''					).input_ids
		lowercase__						=				model.generate(SCREAMING_SNAKE_CASE						,    decoder_start_token_id=0					)
		print(tokenizer.decode(out[0]					)					)






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class 	_a							:
							_lowercase     :      CommonSchedulerState

							# setable values
							_lowercase     :      jnp.ndarray
							_lowercase     :      jnp.ndarray
							_lowercase     :      Optional[int]  				=			None

							@classmethod
							def 				lowerCamelCase_			(							cls:     Dict   ,						UpperCamelCase_:     CommonSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray							)      ->       List[str]:





									"""simple docstring"""







									return cls(common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_							)


@dataclass
class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      DDPMSchedulerState


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__      ):
							_lowercase     :      Tuple  				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowercase     :      jnp.dtype

							@property
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return True

							@register_to_config
							def __init__(							self:     Any   ,						UpperCamelCase_:     int = 1_000   ,						UpperCamelCase_:     float = 0.0001   ,						UpperCamelCase_:     float = 0.02   ,						UpperCamelCase_:     str = "linear"   ,						UpperCamelCase_:     Optional[jnp.ndarray] = None   ,						UpperCamelCase_:     str = "fixed_small"   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     str = "epsilon"   ,						UpperCamelCase_:     jnp.dtype = jnp.floataa   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				dtype

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									if common is None:
											lowercase__						=				CommonSchedulerState.create(self							)

									# standard deviation of the initial noise distribution
									lowercase__						=				jnp.array(1.0   ,						dtype=self.dtype							)

									lowercase__						=				jnp.arange(0   ,						self.config.num_train_timesteps							).round()[::-1]

									return DDPMSchedulerState.create(
									    common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[int] = None							)      ->       jnp.ndarray:





									"""simple docstring"""







									return sample

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Tuple = ()							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									lowercase__						=				self.config.num_train_timesteps // num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# rounding to avoid issues when num_inference_step is power of 3
									lowercase__						=				(jnp.arange(0   ,						UpperCamelCase_							) * step_ratio).round()[::-1]

									return state.replace(
									    num_inference_steps=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     str=None							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)

									# 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
									lowercase__						=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											lowercase__						=				jnp.log(jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)							)
									elif variance_type == "fixed_large":
											lowercase__						=				state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											lowercase__						=				jnp.log(state.common.betas[t]							)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											lowercase__						=				variance
											lowercase__						=				state.common.betas[t]
											lowercase__						=				(predicted_variance + 1) / 2
											lowercase__						=				frac * max_log + (1 - frac) * min_log

									return variance

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[jax.random.KeyArray] = None   ,						UpperCamelCase_:     bool = True   ,						)      ->       Union[FlaxDDPMSchedulerOutput, Tuple]:





									"""simple docstring"""







									lowercase__						=				timestep

									if key is None:
											lowercase__						=				jax.random.PRNGKey(0							)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											lowercase__					,		lowercase__						=				jnp.split(UpperCamelCase_   ,						sample.shape[1]   ,						axis=1							)
									else:
											lowercase__						=				None

									# 1. compute alphas, betas
									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)
									lowercase__						=				1 - alpha_prod_t
									lowercase__						=				1 - alpha_prod_t_prev

									# 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":
											lowercase__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											lowercase__						=				model_output
									elif self.config.prediction_type == "v_prediction":
											lowercase__						=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
											    ''' for the FlaxDDPMScheduler.'''							)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						-1   ,						1							)

									# 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
									lowercase__						=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									lowercase__						=				state.common.alphas[t] ** 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
									lowercase__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
											lowercase__						=				jax.random.split(UpperCamelCase_   ,						num=1							)
											lowercase__						=				jax.random.normal(UpperCamelCase_   ,						shape=model_output.shape   ,						dtype=self.dtype							)
											return (self._get_variance(UpperCamelCase_   ,						UpperCamelCase_   ,						predicted_variance=UpperCamelCase_							) ** 0.5) * noise

									lowercase__						=				jnp.where(t > 0   ,						random_variance()   ,						jnp.zeros(model_output.shape   ,						dtype=self.dtype							)							)

									lowercase__						=				pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_   ,						state=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return add_noise_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return get_velocity_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)


							def __len__(							self:     str							)      ->       List[Any]:





									"""simple docstring"""







									return self.config.num_train_timesteps


