


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

							return flax_key_tuple, flax_tensor






def   _lowercase(					__a				:						Optional[Any]    ,     __a				:						List[str]    ,     __a				:						List[Any]     ):
							if "metadata" in layer:
														a__      =layer.split('metadata'     )
														a__      =''.join(split_layer[0]     )[:-1]
														a__      =[tuple(('metadata' + split_layer[1]).split('/'     )     )]
							elif "kvstore" in layer:
														a__      =layer.split('kvstore'     )
														a__      =''.join(split_layer[0]     )[:-1]
														a__      =[tuple(('kvstore' + split_layer[1]).split('/'     )     )]

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

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

							return curr_real_layer_name, split_layer, content






def   _lowercase(					__a				:						Tuple    ,     __a				:						List[str]     ):
							a__      =rename_keys(__a     )
							a__      ={}
							for k, v in current_block.items():
														a__      =v
							a__      =new_current_block
							torch.save(__a    ,     __a     )






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[str]    ,     __a				:						int    ,     __a				:						Any    ,     __a				:						str = WEIGHTS_NAME     ):
							a__      =convert_file_size_to_int(__a     )
							a__      =[]
							a__      ={}
							a__      =0
							a__      =0

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

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

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

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

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

														a__      =raw_weights.to(getattr(__a    ,     __a     )     )
														current_block_size += weight_size
														total_size += weight_size

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

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

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

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

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

							return metadata, index


if __name__ == "__main__":
	_lowerCAmelCase:						Optional[Any]   				=       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:						Union[str, Any]   				=       parser.parse_args()
	shard_on_the_fly(
	    args.switch_tax_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.max_shard_size,
	    args.dtype,
	)







def   _lowercase(					):
							from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf




@require_vision
@require_torchvision
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

        A__		   =		[Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					) for x in image_inputs]

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		SamProcessor(image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		SamProcessor.from_pretrained(self.tmpdirname,do_normalize=__lowerCamelCase,padding_value=1.0					)

        self.assertEqual(processor.image_processor.to_json_string(),image_processor_add_kwargs.to_json_string()					)
        self.assertIsInstance(processor.image_processor,__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''np'''					)

        input_feat_extract.pop('''original_sizes'''					)  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('''reshaped_input_sizes'''					)  # pop original_sizes as it is popped in the processor

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum(),input_processor[key].sum(),delta=1E-2					)




 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		[torch.ones((1, 3, 5, 5)					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		processor.post_process_masks(
            __lowerCamelCase,torch.tensor(__lowerCamelCase					),torch.tensor(__lowerCamelCase					)					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        # should also work with np
        A__		   =		[np.ones((1, 3, 5, 5)					)]
        A__		   =		processor.post_process_masks(__lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					)					)

        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		[[1, 0], [0, 1]]
        with self.assertRaises(__lowerCamelCase					):
               A__		   =		processor.post_process_masks(__lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					)					)




@require_vision
@require_tf
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

        A__		   =		[Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					) for x in image_inputs]

        return image_inputs



 def 							UpperCamelCase     (				self					):
        A__		   =		SamProcessor(image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

        A__		   =		self.get_image_processor(do_normalize=__lowerCamelCase,padding_value=1.0					)

        A__		   =		SamProcessor.from_pretrained(self.tmpdirname,do_normalize=__lowerCamelCase,padding_value=1.0					)

        self.assertEqual(processor.image_processor.to_json_string(),image_processor_add_kwargs.to_json_string()					)
        self.assertIsInstance(processor.image_processor,__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''np'''					)
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''np'''					)

        input_feat_extract.pop('''original_sizes'''					)  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('''reshaped_input_sizes'''					)  # pop reshaped_input_sizes as it is popped in the processor

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum(),input_processor[key].sum(),delta=1E-2					)




 @require_tf
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		[tf.ones((1, 3, 5, 5)					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''tf'''					)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		processor.post_process_masks(
            __lowerCamelCase,tf.convert_to_tensor(__lowerCamelCase					),tf.convert_to_tensor(__lowerCamelCase					),return_tensors='''tf''',)
        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        # should also work with np
        A__		   =		[np.ones((1, 3, 5, 5)					)]
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					),return_tensors='''tf'''					)

        self.assertEqual(masks[0].shape,(1, 3, 1764, 2646)					)

        A__		   =		[[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError					):
               A__		   =		processor.post_process_masks(
                   __lowerCamelCase,np.array(__lowerCamelCase					),np.array(__lowerCamelCase					),return_tensors='''tf'''					)




@require_vision
@require_torchvision
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        A__		   =		tempfile.mkdtemp()
        A__		   =		SamImageProcessor()
        A__		   =		SamProcessor(__lowerCamelCase					)
        processor.save_pretrained(self.tmpdirname					)



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname,**__lowerCamelCase					).image_processor



 def 							UpperCamelCase     (				self					):
        shutil.rmtree(self.tmpdirname					)



 def 							UpperCamelCase     (				self					):
        A__		   =		[np.random.randint(255,size=(3, 30, 400),dtype=np.uinta					)]

        A__		   =		[Image.fromarray(np.moveaxis(__lowerCamelCase,0,-1					)					) for x in image_inputs]

        return image_inputs



 @is_pt_tf_cross_test
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)
        A__		   =		np.random.randint(0,2,size=(1, 3, 5, 5)					).astype(np.floataa					)
        A__		   =		[tf.convert_to_tensor(__lowerCamelCase					)]
        A__		   =		[torch.tensor(__lowerCamelCase					)]

        A__		   =		[[1764, 2646]]

        A__		   =		[[683, 1024]]
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''tf'''					)
        A__		   =		processor.post_process_masks(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,return_tensors='''pt'''					)

        self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()					)					)




 @is_pt_tf_cross_test
 def 							UpperCamelCase     (				self					):
        A__		   =		self.get_image_processor()

        A__		   =		SamProcessor(image_processor=__lowerCamelCase					)

        A__		   =		self.prepare_image_inputs()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''pt'''					)['''pixel_values'''].numpy()
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''pt'''					)['''pixel_values'''].numpy()

        A__		   =		image_processor(__lowerCamelCase,return_tensors='''tf'''					)['''pixel_values'''].numpy()
        A__		   =		processor(images=__lowerCamelCase,return_tensors='''tf'''					)['''pixel_values'''].numpy()

        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase					)					)
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase					)					)
        self.assertTrue(np.allclose(__lowerCamelCase,__lowerCamelCase					)					)





