






"""simple docstring"""
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 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		A_		:						Union[str, Any]	    =   flax_key_tuple[:-1] + ("""weight""",)
		A_		:						Dict	    =   torch.permute(snake_case__   ,   (0, 2, 1)					)
	elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__					):
		# linear layer
		A_		:						Tuple	    =   flax_key_tuple[:-1] + ("""weight""",)
		A_		:						Union[str, Any]	    =   flax_tensor.T
	elif flax_key_tuple[-1] in ["scale", "embedding"]:
		A_		:						Optional[int]	    =   flax_key_tuple[:-1] + ("""weight""",)

	return flax_key_tuple, flax_tensor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	if "metadata" in layer:
		A_		:						str	    =   layer.split("""metadata"""					)
		A_		:						Optional[int]	    =   """""".join(split_layer[0]					)[:-1]
		A_		:						List[str]	    =   [tuple(("""metadata""" + split_layer[1]).split("""/"""					)					)]
	elif "kvstore" in layer:
		A_		:						List[Any]	    =   layer.split("""kvstore"""					)
		A_		:						List[str]	    =   """""".join(split_layer[0]					)[:-1]
		A_		:						List[str]	    =   [tuple(("""kvstore""" + split_layer[1]).split("""/"""					)					)]

	else:
		A_		:						Union[str, Any]	    =   layer.split("""/"""					)
		A_		:						Optional[Any]	    =   """/""".join(split_layer[:-1]					)
		A_		:						Tuple	    =   (split_layer[-1],)

	if "kvstore/path" in layer:
		A_		:						Union[str, Any]	    =   F"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
	elif "kvstore/driver" in layer:
		A_		:						List[str]	    =   """file"""
	else:
		A_		:						str	    =   checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   rename_keys(snake_case__					)
	A_		:						Dict	    =   {}
	for k, v in current_block.items():
		A_		:						Union[str, Any]	    =   v
	A_		:						List[Any]	    =   new_current_block
	torch.save(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__ = WEIGHTS_NAME					):
	A_		:						List[Any]	    =   convert_file_size_to_int(snake_case__					)
	A_		:						Tuple	    =   []
	A_		:						Union[str, Any]	    =   {}
	A_		:						Optional[Any]	    =   0
	A_		:						Optional[Any]	    =   0

	os.makedirs(snake_case__   ,   exist_ok=snake_case__					)
	with gfile.GFile(switch_checkpoint_path + """/checkpoint"""   ,   """rb"""					) as fp:
		A_		:						Optional[int]	    =   serialization.msgpack_restore(fp.read()					)["""optimizer"""]["""target"""]
		A_		:						Tuple	    =   flatten_dict(snake_case__   ,   sep="""/"""					)

	A_		:						List[str]	    =   {}
	for layer in checkpoint_info.keys():
		A_    ,     A_    ,     A_		:						List[str]	    =   get_key_and_tensorstore_dict(
		    snake_case__   ,   snake_case__   ,   snake_case__					)
		if curr_real_layer_name in all_layers:
			A_		:						Any	    =   content
		else:
			A_		:						Union[str, Any]	    =   {split_layer[-1]: content}

	for key in all_layers.keys():
		# open tensorstore file
		A_		:						List[str]	    =   ts.open(unflatten_dict(all_layers[key]					)					).result().read().result()
		A_		:						Dict	    =   torch.tensor(snake_case__					)
		A_		:						Optional[int]	    =   raw_weights.numel() * dtype_byte_size(raw_weights.dtype					)

		# use the renaming pattern from the small conversion scripts
		A_    ,     A_		:						Union[str, Any]	    =   rename_base_flax_keys(tuple(key.split("""/"""					)					)   ,   snake_case__					)
		A_		:						Union[str, Any]	    =   """/""".join(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:
			A_		:						Union[str, Any]	    =   os.path.join(
			    snake_case__   ,   weights_name.replace(""".bin"""   ,   F"""-{len(snake_case__					)+1:05d}-of-???.bin"""					)					)
			rename_and_save_block(snake_case__   ,   snake_case__					)
			sharded_state_dicts.append(current_block.keys()					)
			del current_block
			A_		:						Optional[Any]	    =   {}
			A_		:						Dict	    =   0

		A_		:						Any	    =   raw_weights.to(getattr(snake_case__   ,   snake_case__					)					)
		current_block_size += weight_size
		total_size += weight_size

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

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

	# Otherwise, let's build the index
	A_		:						Optional[Any]	    =   {}
	A_		:						int	    =   {}
	for idx, shard in enumerate(snake_case__					):
		A_		:						int	    =   weights_name.replace(
		    """.bin"""   ,   F"""-{idx+1:05d}-of-{len(snake_case__					):05d}.bin"""					)  # len(sharded_state_dicts):05d}
		A_		:						Union[str, Any]	    =   os.path.join(snake_case__   ,   weights_name.replace(""".bin"""   ,   F"""-{idx+1:05d}-of-???.bin"""					)					)
		os.rename(snake_case__   ,   os.path.join(snake_case__   ,   snake_case__					)					)
		A_		:						str	    =   shard
		for key in shard:
			A_		:						str	    =   shard_file

    # Add the metadata
	A_		:						List[str]	    =   {"""total_size""": total_size}
	A_		:						int	    =   {"""metadata""": metadata, """weight_map""": weight_map}

	with open(os.path.join(snake_case__   ,   snake_case__					)   ,   """w"""   ,   encoding="""utf-8"""					) as f:
		A_		:						Union[str, Any]	    =   json.dumps(snake_case__   ,   indent=2   ,   sort_keys=snake_case__					) + """\n"""
		f.write(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 			__UpperCamelCase	(		):
	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

	A_		:						Tuple	    =   SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""					)
	config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""					)
	A_		:						Optional[Any]	    =   SwitchTransformersForConditionalGeneration.from_pretrained(
	    """/home/arthur_huggingface_co/transformers/switch_converted"""   ,   device_map="""auto"""					)

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

	A_		:						int	    =   tokenizer(snake_case__   ,   return_tensors="""pt"""					).input_ids
	A_		:						List[Any]	    =   model.generate(snake_case__   ,   decoder_start_token_id=0					)
	print(tokenizer.decode(out[0]					)					)









"""simple docstring"""
import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				PriorTransformer
	_A       :				Dict    =				"""hidden_states"""





	@property
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   4
		A_		:						Dict	    =   8
		A_		:						str	    =   7

		A_		:						Dict	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						str	    =   floats_tensor((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						List[str]	    =   floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   4
		A_		:						Tuple	    =   8
		A_		:						Tuple	    =   7

		A_		:						int	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						List[str]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	@property
	def    lowerCamelCase(self							):
		return (4, 8)





	def    lowerCamelCase(self							):
		A_		:						int	    =   {
		    """num_attention_heads""": 2,
		    """attention_head_dim""": 4,
		    """num_layers""": 2,
		    """embedding_dim""": 8,
		    """num_embeddings""": 7,
		    """additional_embeddings""": 4,
		}
		A_		:						Dict	    =   self.dummy_input
		return init_dict, inputs_dict





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Optional[int]	    =   PriorTransformer.from_pretrained(
		    """hf-internal-testing/prior-dummy"""     ,     output_loading_info=lowerCAmelCase_							)
		self.assertIsNotNone(lowerCAmelCase_							)
		self.assertEqual(len(loading_info["""missing_keys"""]							)     ,     0							)

		model.to(lowerCAmelCase_							)
		A_		:						List[Any]	    =   model(**self.dummy_input							)[0]

		assert hidden_states is not None, "Make sure output is not None"





	def    lowerCamelCase(self							):
		A_    ,     A_		:						Union[str, Any]	    =   self.prepare_init_args_and_inputs_for_common()

		A_		:						Union[str, Any]	    =   self.model_class(**lowerCAmelCase_							)
		A_		:						Tuple	    =   inspect.signature(model.forward							)
		# signature.parameters is an OrderedDict => so arg_names order is deterministic
		A_		:						Tuple	    =   [*signature.parameters.keys()]

		A_		:						List[str]	    =   ["""hidden_states""", """timestep"""]
		self.assertListEqual(arg_names[:2]     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""							)
		A_		:						Optional[Any]	    =   model.to(lowerCAmelCase_							)

		if hasattr(lowerCAmelCase_     ,     """set_default_attn_processor"""							):
			model.set_default_attn_processor()

		A_		:						int	    =   self.get_dummy_seed_input()

		with torch.no_grad():
			A_		:						int	    =   model(**lowerCAmelCase_							)[0]

		A_		:						Any	    =   output[0, :5].flatten().cpu()
		print(lowerCAmelCase_							)

		# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
		# the expected output slices are not the same for CPU and GPU.
		A_		:						Optional[int]	    =   torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]							)
		self.assertTrue(torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     rtol=1e-2							)							)






@slow
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=768     ,     lowerCAmelCase_=77     ,     lowerCAmelCase_=0							):
		torch.manual_seed(lowerCAmelCase_							)
		A_		:						Tuple	    =   batch_size
		A_		:						Any	    =   embedding_dim
		A_		:						Tuple	    =   num_embeddings

		A_		:						Any	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)

		A_		:						Union[str, Any]	    =   torch.randn((batch_size, embedding_dim)							).to(lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowerCAmelCase_							)

		return {
		    "hidden_states": hidden_states,
		    "timestep": 2,
		    "proj_embedding": proj_embedding,
		    "encoder_hidden_states": encoder_hidden_states,
		}





	def    lowerCamelCase(self							):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
	        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
	        # fmt: on
	    ]							)
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""     ,     subfolder="""prior"""							)
		model.to(lowerCAmelCase_							)
		A_		:						int	    =   self.get_dummy_seed_input(seed=lowerCAmelCase_							)

		with torch.no_grad():
			A_		:						Tuple	    =   model(**lowerCAmelCase_							)[0]

		assert list(sample.shape							) == [1, 768]

		A_		:						Tuple	    =   sample[0, :8].flatten().cpu()
		print(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.tensor(lowerCAmelCase_							)

		assert torch_all_close(lowerCAmelCase_     ,     lowerCAmelCase_     ,     atol=1e-3							)

