



import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Optional[int] =       ProphetNetTokenizer
							A				:List[Any] =       False



							def 		_A	(							self				):







											"""simple docstring"""



											super().setUp()

											a__	:							Any      						= [
											    "[UNK]",
											    "[CLS]",
											    "[SEP]",
											    "[PAD]",
											    "[MASK]",
											    "want",
											    "##want",
											    "##ed",
											    "wa",
											    "un",
											    "runn",
											    "##ing",
											    ",",
											    "low",
											    "lowest",
											]
											a__	:							int      						= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]				)
											with open(self.vocab_file ,					"w" ,					encoding="utf-8"				) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= "UNwant\u00E9d,running"
											a__	:							Optional[int]      						= "unwanted, running"
											return input_text, output_text



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.tokenizer_class(self.vocab_file				)

											a__	:							int      						= tokenizer.tokenize("UNwant\u00E9d,running"				)
											self.assertListEqual(__UpperCAmelCase ,					["un", "##want", "##ed", ",", "runn", "##ing"]				)
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				) ,					[9, 6, 7, 12, 10, 11]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"				) ,					["ah", "\u535A", "\u63A8", "zz"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= BasicTokenizer(do_lower_case=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "				) ,					["hello", "!", "how", "are", "you", "?"]				)
											self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,					["hello"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,					["hällo", "!", "how", "are", "you", "?"]				)
											self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,					["h\u00E9llo"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,					["hallo", "!", "how", "are", "you", "?"]				)
											self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,					["hello"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= BasicTokenizer(do_lower_case=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,					["hallo", "!", "how", "are", "you", "?"]				)
											self.assertListEqual(tokenizer.tokenize("H\u00E9llo"				) ,					["hello"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= BasicTokenizer(do_lower_case=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "				) ,					["HeLLo", "!", "how", "Are", "yoU", "?"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,					["HäLLo", "!", "how", "Are", "yoU", "?"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= BasicTokenizer(do_lower_case=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "				) ,					["HaLLo", "!", "how", "Are", "yoU", "?"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= BasicTokenizer(do_lower_case=__UpperCAmelCase ,					never_split=["[UNK]"]				)

											self.assertListEqual(
											    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"				) ,					["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

											a__	:							List[str]      						= {}
											for i, token in enumerate(__UpperCAmelCase				):
															a__	:							str      						= i
											a__	:							int      						= WordpieceTokenizer(vocab=__UpperCAmelCase ,					unk_token="[UNK]"				)

											self.assertListEqual(tokenizer.tokenize(""				) ,					[]				)

											self.assertListEqual(tokenizer.tokenize("unwanted running"				) ,					["un", "##want", "##ed", "runn", "##ing"]				)

											self.assertListEqual(tokenizer.tokenize("unwantedX running"				) ,					["[UNK]", "runn", "##ing"]				)



							@require_torch
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"				)

											a__	:							Any      						= ["A long paragraph for summarization.", "Another paragraph for summarization."]
											a__	:							Optional[Any]      						= [1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
											a__	:							Optional[int]      						= tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors="pt"				)
											self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							Optional[int]      						= list(batch.input_ids.numpy()[0]				)
											self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase				)

											self.assertEqual((2, 9) ,					batch.input_ids.shape				)
											self.assertEqual((2, 9) ,					batch.attention_mask.shape				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.assertTrue(_is_whitespace(" "				)				)
											self.assertTrue(_is_whitespace("\t"				)				)
											self.assertTrue(_is_whitespace("\r"				)				)
											self.assertTrue(_is_whitespace("\n"				)				)
											self.assertTrue(_is_whitespace("\u00A0"				)				)

											self.assertFalse(_is_whitespace("A"				)				)
											self.assertFalse(_is_whitespace("-"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.assertTrue(_is_control("\u0005"				)				)

											self.assertFalse(_is_control("A"				)				)
											self.assertFalse(_is_control(" "				)				)
											self.assertFalse(_is_control("\t"				)				)
											self.assertFalse(_is_control("\r"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.assertTrue(_is_punctuation("-"				)				)
											self.assertTrue(_is_punctuation("$"				)				)
											self.assertTrue(_is_punctuation("`"				)				)
											self.assertTrue(_is_punctuation("."				)				)

											self.assertFalse(_is_punctuation("A"				)				)
											self.assertFalse(_is_punctuation(" "				)				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"				)

											a__	:							Dict      						= tokenizer.encode("sequence builders" ,					add_special_tokens=__UpperCAmelCase				)
											a__	:							int      						= tokenizer.encode("multi-sequence build" ,					add_special_tokens=__UpperCAmelCase				)

											a__	:							Optional[Any]      						= tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase				)
											a__	:							Optional[int]      						= tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase ,					__UpperCAmelCase				)

											assert encoded_sentence == text + [102]
											assert encoded_pair == text + [102] + text_a + [102]







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

				return flax_key_tuple, flax_tensor






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

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

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

				return curr_real_layer_name, split_layer, content






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							List[Any]      						= rename_keys(__UpperCamelCase )
				a__	:							Tuple      						= {}
				for k, v in current_block.items():
								a__	:							str      						= v
				a__	:							Optional[int]      						= new_current_block
				torch.save(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Any:
				a__	:							Optional[Any]      						= convert_file_size_to_int(__UpperCamelCase )
				a__	:							Tuple      						= []
				a__	:							Dict      						= {}
				a__	:							str      						= 0
				a__	:							Tuple      						= 0

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

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

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

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

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

								a__	:							List[str]      						= raw_weights.to(getattr(__UpperCamelCase	,				__UpperCamelCase ) )
								current_block_size += weight_size
								total_size += weight_size

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

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

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

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

				with open(os.path.join(__UpperCamelCase	,				__UpperCamelCase )	,				"w"	,				encoding="utf-8" ) as f:
								a__	:							List[str]      						= json.dumps(__UpperCamelCase	,				indent=2	,				sort_keys=__UpperCamelCase ) + "\n"
								f.write(__UpperCamelCase )

				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     SCREAMING_SNAKE_CASE(      )	->						int:
				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

				a__	:							Optional[int]      						= tokenizer(__UpperCamelCase	,				return_tensors="pt" ).input_ids
				a__	:							Optional[Any]      						= model.generate(__UpperCamelCase	,				decoder_start_token_id=0 )
				print(tokenizer.decode(out[0] ) )


