



"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFRoFormerForCausalLM,
			    TFRoFormerForMaskedLM,
			    TFRoFormerForMultipleChoice,
			    TFRoFormerForQuestionAnswering,
			    TFRoFormerForSequenceClassification,
			    TFRoFormerForTokenClassification,
			    TFRoFormerModel,
			)
			from transformers.models.roformer.modeling_tf_roformer import (
			    TFRoFormerSelfAttention,
			    TFRoFormerSinusoidalPositionalEmbedding,
			)

class       _a  :



			def __init__(			self							:				str   ,	_lowercase							:				int   ,	_lowercase							:				int=13   ,	_lowercase							:				Any=7   ,	_lowercase							:				List[Any]=True   ,	_lowercase							:				int=True   ,	_lowercase							:				str=True   ,	_lowercase							:				Union[str, Any]=True   ,	_lowercase							:				Any=99   ,	_lowercase							:				Union[str, Any]=32   ,	_lowercase							:				int=2   ,	_lowercase							:				Union[str, Any]=4   ,	_lowercase							:				List[Any]=37   ,	_lowercase							:				Optional[Any]="gelu"   ,	_lowercase							:				int=0.1   ,	_lowercase							:				List[Any]=0.1   ,	_lowercase							:				str=512   ,	_lowercase							:				Optional[Any]=16   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				Union[str, Any]=0.02   ,	_lowercase							:				Tuple=3   ,	_lowercase							:				Tuple=4   ,	_lowercase							:				Any=None   ,	)							->					List[str]:
									snake_case   :			Optional[Any]						    = parent
									snake_case   :			int						    = 13
									snake_case   :			Dict						    = 7
									snake_case   :			Dict						    = True
									snake_case   :			Union[str, Any]						    = True
									snake_case   :			int						    = True
									snake_case   :			Union[str, Any]						    = True
									snake_case   :			Tuple						    = 99
									snake_case   :			int						    = 32
									snake_case   :			Dict						    = 2
									snake_case   :			Tuple						    = 4
									snake_case   :			List[str]						    = 37
									snake_case   :			Optional[Any]						    = "gelu"
									snake_case   :			List[str]						    = 0.1
									snake_case   :			Dict						    = 0.1
									snake_case   :			List[str]						    = 512
									snake_case   :			Any						    = 16
									snake_case   :			Dict						    = 2
									snake_case   :			Dict						    = 0.02
									snake_case   :			Union[str, Any]						    = 3
									snake_case   :			str						    = 4
									snake_case   :			Tuple						    = None



			def 				__lowercase					(			self							:				Dict		)							->					int:
									snake_case   :			Union[str, Any]						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)

									snake_case   :			Dict						    = None
									if self.use_input_mask:
															snake_case   :			str						    = random_attention_mask([self.batch_size, self.seq_length]		)

									snake_case   :			Tuple						    = None
									if self.use_token_type_ids:
															snake_case   :			List[str]						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.type_vocab_size		)

									snake_case   :			Tuple						    = None
									snake_case   :			str						    = None
									snake_case   :			Dict						    = None
									if self.use_labels:
															snake_case   :			Any						    = ids_tensor([self.batch_size]   ,	self.type_sequence_label_size		)
															snake_case   :			int						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels		)
															snake_case   :			Any						    = ids_tensor([self.batch_size]   ,	self.num_choices		)

									snake_case   :			List[str]						    = RoFormerConfig(
									    vocab_size=self.vocab_size   ,	hidden_size=self.hidden_size   ,	num_hidden_layers=self.num_hidden_layers   ,	num_attention_heads=self.num_attention_heads   ,	intermediate_size=self.intermediate_size   ,	hidden_act=self.hidden_act   ,	hidden_dropout_prob=self.hidden_dropout_prob   ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,	max_position_embeddings=self.max_position_embeddings   ,	type_vocab_size=self.type_vocab_size   ,	initializer_range=self.initializer_range   ,	return_dict=_lowercase   ,	)

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				Dict   ,	_lowercase							:				List[str]   ,	_lowercase							:				Tuple		)							->					List[str]:
									snake_case   :			int						    = TFRoFormerModel(config=_lowercase		)
									snake_case   :			Optional[Any]						    = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

									snake_case   :			Union[str, Any]						    = [input_ids, input_mask]
									snake_case   :			Dict						    = model(_lowercase		)

									snake_case   :			Any						    = model(_lowercase		)

									self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)		)



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				Tuple   ,	_lowercase							:				List[str]   ,	_lowercase							:				Tuple   ,	_lowercase							:				int   ,	_lowercase							:				Any   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				int		)							->					Tuple:
									snake_case   :			Optional[int]						    = True
									snake_case   :			List[Any]						    = TFRoFormerForCausalLM(config=_lowercase		)
									snake_case   :			List[Any]						    = {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									snake_case   :			Tuple						    = model(_lowercase		)["logits"]
									self.parent.assertListEqual(
									    list(prediction_scores.numpy().shape		)   ,	[self.batch_size, self.seq_length, self.vocab_size]		)



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				Tuple   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Any   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Any   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Union[str, Any]		)							->					Dict:
									snake_case   :			List[str]						    = TFRoFormerForMaskedLM(config=_lowercase		)
									snake_case   :			Optional[Any]						    = {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									snake_case   :			str						    = model(_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				int   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Dict   ,	_lowercase							:				Dict   ,	_lowercase							:				Union[str, Any]		)							->					Optional[Any]:
									snake_case   :			Optional[Any]						    = self.num_labels
									snake_case   :			Dict						    = TFRoFormerForSequenceClassification(config=_lowercase		)
									snake_case   :			Optional[int]						    = {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}

									snake_case   :			List[str]						    = model(_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)		)



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				str   ,	_lowercase							:				Tuple		)							->					Optional[int]:
									snake_case   :			List[Any]						    = self.num_choices
									snake_case   :			List[Any]						    = TFRoFormerForMultipleChoice(config=_lowercase		)
									snake_case   :			Union[str, Any]						    = tf.tile(tf.expand_dims(_lowercase   ,	1		)   ,	(1, self.num_choices, 1)		)
									snake_case   :			Optional[Any]						    = tf.tile(tf.expand_dims(_lowercase   ,	1		)   ,	(1, self.num_choices, 1)		)
									snake_case   :			List[Any]						    = tf.tile(tf.expand_dims(_lowercase   ,	1		)   ,	(1, self.num_choices, 1)		)
									snake_case   :			Dict						    = {
									    "input_ids": multiple_choice_inputs_ids,
									    "attention_mask": multiple_choice_input_mask,
									    "token_type_ids": multiple_choice_token_type_ids,
									}
									snake_case   :			Any						    = model(_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_choices)		)



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				str   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				List[Any]		)							->					str:
									snake_case   :			Any						    = self.num_labels
									snake_case   :			str						    = TFRoFormerForTokenClassification(config=_lowercase		)
									snake_case   :			Optional[int]						    = {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									snake_case   :			Optional[int]						    = model(_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.num_labels)		)



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Tuple   ,	_lowercase							:				List[str]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Dict   ,	_lowercase							:				List[str]   ,	_lowercase							:				Any		)							->					List[Any]:
									snake_case   :			int						    = TFRoFormerForQuestionAnswering(config=_lowercase		)
									snake_case   :			Optional[int]						    = {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}

									snake_case   :			List[str]						    = model(_lowercase		)
									self.parent.assertEqual(result.start_logits.shape   ,	(self.batch_size, self.seq_length)		)
									self.parent.assertEqual(result.end_logits.shape   ,	(self.batch_size, self.seq_length)		)



			def 				__lowercase					(			self							:				Optional[Any]		)							->					Tuple:
									snake_case   :			Tuple						    = self.prepare_config_and_inputs()
									(
									    (
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			
									)   :			List[Any]						    = config_and_inputs
									snake_case   :			Optional[Any]						    = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_tf
class       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     (
			    (
			        TFRoFormerModel,
			        TFRoFormerForCausalLM,
			        TFRoFormerForMaskedLM,
			        TFRoFormerForQuestionAnswering,
			        TFRoFormerForSequenceClassification,
			        TFRoFormerForTokenClassification,
			        TFRoFormerForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			__magic_name__     					=     (
			    {
			        """feature-extraction""": TFRoFormerModel,
			        """fill-mask""": TFRoFormerForMaskedLM,
			        """question-answering""": TFRoFormerForQuestionAnswering,
			        """text-classification""": TFRoFormerForSequenceClassification,
			        """text-generation""": TFRoFormerForCausalLM,
			        """token-classification""": TFRoFormerForTokenClassification,
			        """zero-shot""": TFRoFormerForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			__magic_name__     					=     False
			__magic_name__     					=     False



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				List[str]   ,	_lowercase							:				str   ,	_lowercase							:				str   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				List[Any]		)							->					Dict:
									if pipeline_test_casse_name == "TextGenerationPipelineTests":
															return True

									return False



			def 				__lowercase					(			self							:				int		)							->					str:
									snake_case   :			int						    = TFRoFormerModelTester(self		)
									snake_case   :			Optional[Any]						    = ConfigTester(self   ,	config_class=_lowercase   ,	hidden_size=37		)



			def 				__lowercase					(			self							:				Optional[Any]		)							->					List[str]:
									self.config_tester.run_common_tests()



			def 				__lowercase					(			self							:				List[str]		)							->					int:
									snake_case   :			Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									snake_case   :			Any						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					Dict:
									snake_case   :			Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head(*_lowercase		)



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									snake_case   :			Any						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_lowercase		)



			def 				__lowercase					(			self							:				Any		)							->					List[str]:
									snake_case   :			Any						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					Any:
									snake_case   :			Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_lowercase		)



			def 				__lowercase					(			self							:				Any		)							->					Tuple:
									snake_case   :			Dict						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_lowercase		)



			@slow
			def 				__lowercase					(			self							:				Tuple		)							->					Any:
									snake_case   :			Dict						    = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"		)
									self.assertIsNotNone(_lowercase		)



@require_tf
class       _a  ( unittest.TestCase):



			@slow
			def 				__lowercase					(			self							:				List[Any]		)							->					Optional[int]:
									snake_case   :			Dict						    = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
									snake_case   :			List[str]						    = tf.constant([[0, 1, 2, 3, 4, 5]]		)
									snake_case   :			int						    = model(_lowercase		)[0]

									# TODO Replace vocab size
									snake_case   :			Union[str, Any]						    = 50000

									snake_case   :			Optional[int]						    = [1, 6, vocab_size]
									self.assertEqual(output.shape   ,	_lowercase		)

									print(output[:, :3, :3]		)

									# TODO Replace values below with what was printed above.
									snake_case   :			Any						    = tf.constant(
									    [
									        [
									            [-0.12053341, -1.0264901, 0.29221946],
									            [-1.5133783, 0.197433, 0.15190607],
									            [-5.0135403, -3.900256, -0.84038764],
									        ]
									    ]		)
									tf.debugging.assert_near(output[:, :3, :3]   ,	_lowercase   ,	atol=1E-4		)



@require_tf
class       _a  ( unittest.TestCase):
			__magic_name__     					=     1e-4



			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									snake_case   :			str						    = tf.constant([[4, 10]]		)
									snake_case   :			List[Any]						    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6   ,	embedding_dim=6		)

									snake_case   :			List[str]						    = emba(input_ids.shape		)
									snake_case   :			Optional[Any]						    = tf.constant(
									    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]		)

									tf.debugging.assert_near(_lowercase   ,	_lowercase   ,	atol=self.tolerance		)



			def 				__lowercase					(			self							:				int		)							->					Dict:
									snake_case   :			Dict						    = tf.constant(
									    [
									        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
									        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
									        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
									    ]		)
									snake_case   :			Optional[int]						    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512   ,	embedding_dim=512		)
									emba([2, 16, 512]		)
									snake_case   :			Any						    = emba.weight[:3, :5]

									tf.debugging.assert_near(_lowercase   ,	_lowercase   ,	atol=self.tolerance		)



@require_tf
class       _a  ( unittest.TestCase):
			__magic_name__     					=     1e-4



			def 				__lowercase					(			self							:				Dict		)							->					Dict:
									# 2,12,16,64
									snake_case   :			Union[str, Any]						    = tf.reshape(tf.range(2 * 12 * 16 * 64   ,	dtype=tf.floataa		)   ,	shape=(2, 12, 16, 64)		) / 100

									snake_case   :			Dict						    = -tf.reshape(tf.range(2 * 12 * 16 * 64   ,	dtype=tf.floataa		)   ,	shape=(2, 12, 16, 64)		) / 100

									snake_case   :			Union[str, Any]						    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32   ,	embedding_dim=64		)
									snake_case   :			Optional[int]						    = embed_positions([2, 16, 768]		)[None, None, :, :]

									snake_case    ,			snake_case   :			Optional[Any]						    = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
									    _lowercase   ,	_lowercase   ,	_lowercase		)

									snake_case   :			Dict						    = tf.constant(
									    [
									        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
									        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
									        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
									        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
									        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
									        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
									    ]		)
									snake_case   :			Any						    = tf.constant(
									    [
									        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
									        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
									        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
									        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
									        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
									        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
									    ]		)

									tf.debugging.assert_near(query_layer[0, 0, :6, :8]   ,	_lowercase   ,	atol=self.tolerance		)
									tf.debugging.assert_near(key_layer[0, 0, :6, :8]   ,	_lowercase   ,	atol=self.tolerance		)








"""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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												snake_case   :			List[str]						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Union[str, Any]						    = torch.permute(lowerCamelCase_     ,		(0, 2, 1) )
						elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_ ):
												# linear layer
												snake_case   :			int						    = flax_key_tuple[:-1] + ("weight",)
												snake_case   :			Dict						    = flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												snake_case   :			Any						    = flax_key_tuple[:-1] + ("weight",)

						return flax_key_tuple, flax_tensor

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						if "metadata" in layer:
												snake_case   :			Dict						    = layer.split("metadata" )
												snake_case   :			Optional[Any]						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Any						    = [tuple(("metadata" + split_layer[1]).split("/" ) )]
						elif "kvstore" in layer:
												snake_case   :			List[str]						    = layer.split("kvstore" )
												snake_case   :			Tuple						    = "".join(split_layer[0] )[:-1]
												snake_case   :			Union[str, Any]						    = [tuple(("kvstore" + split_layer[1]).split("/" ) )]

						else:
												snake_case   :			List[Any]						    = layer.split("/" )
												snake_case   :			Union[str, Any]						    = "/".join(split_layer[:-1] )
												snake_case   :			int						    = (split_layer[-1],)

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

						return curr_real_layer_name, split_layer, content

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = rename_keys(lowerCamelCase_ )
						snake_case   :			str						    = {}
						for k, v in current_block.items():
												snake_case   :			List[str]						    = v
						snake_case   :			List[str]						    = new_current_block
						torch.save(lowerCamelCase_     ,		lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Tuple     ,		lowerCamelCase_:							Optional[Any]     ,		lowerCamelCase_:							Dict     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							str = WEIGHTS_NAME ):
						"""simple docstring"""






						snake_case   :			List[str]						    = convert_file_size_to_int(lowerCamelCase_ )
						snake_case   :			List[Any]						    = []
						snake_case   :			Dict						    = {}
						snake_case   :			str						    = 0
						snake_case   :			List[str]						    = 0

						os.makedirs(lowerCamelCase_     ,		exist_ok=lowerCamelCase_ )
						with gfile.GFile(switch_checkpoint_path + "/checkpoint"     ,		"rb" ) as fp:
												snake_case   :			List[Any]						    = serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
												snake_case   :			Union[str, Any]						    = flatten_dict(lowerCamelCase_     ,		sep="/" )

						snake_case   :			Optional[int]						    = {}
						for layer in checkpoint_info.keys():
												snake_case    ,			snake_case    ,			snake_case   :			Union[str, Any]						    = get_key_and_tensorstore_dict(
												    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												if curr_real_layer_name in all_layers:
																		snake_case   :			str						    = content
												else:
																		snake_case   :			Any						    = {split_layer[-1]: content}

						for key in all_layers.keys():
												# open tensorstore file
												snake_case   :			Optional[Any]						    = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
												snake_case   :			Tuple						    = torch.tensor(lowerCamelCase_ )
												snake_case   :			str						    = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

												# use the renaming pattern from the small conversion scripts
												snake_case    ,			snake_case   :			Dict						    = rename_base_flax_keys(tuple(key.split("/" ) )     ,		lowerCamelCase_ )
												snake_case   :			Union[str, Any]						    = "/".join(lowerCamelCase_ )

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

												snake_case   :			Any						    = raw_weights.to(getattr(lowerCamelCase_     ,		lowerCamelCase_ ) )
												current_block_size += weight_size
												total_size += weight_size

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

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

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

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

						with open(os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )     ,		"w"     ,		encoding="utf-8" ) as f:
												snake_case   :			Tuple						    = json.dumps(lowerCamelCase_     ,		indent=2     ,		sort_keys=lowerCamelCase_ ) + "\n"
												f.write(lowerCamelCase_ )

						return metadata, index


if __name__ == "__main__":
			A		=						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.',
			)
			A		=						parser.parse_args()
			shard_on_the_fly(
			    args.switch_tax_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.max_shard_size,
			    args.dtype,
			)

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

						snake_case   :			Dict						    = tokenizer(lowerCamelCase_     ,		return_tensors="pt" ).input_ids
						snake_case   :			Optional[int]						    = model.generate(lowerCamelCase_     ,		decoder_start_token_id=0 )
						print(tokenizer.decode(out[0] ) )



