

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	A_ :				Tuple						 =    'pt'
elif is_tf_available():
	A_ :				Optional[int]						 =    'tf'
else:
	A_ :				str						 =    'jax'



class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Dict													= PerceiverTokenizer
					UpperCAmelCase__:     str													= False






					def 						__A    (				self    ):
						super().setUp()
						A__							:					int       =	PerceiverTokenizer()
						tokenizer.save_pretrained(self.tmpdirname    )






					@cached_property
					def 						__A    (				self    ):
						return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""    )






					def 						__A    (				self					, **A__    ):
						return self.tokenizer_class.from_pretrained(self.tmpdirname					, **A__    )






					def 						__A    (				self					, A__					, A__=False					, A__=20					, A__=5    ):
						# XXX The default common tokenizer tests assume that every ID is decodable on its own.
						# This assumption is invalid for Perceiver because single bytes might not be
						# valid utf-8 (byte 128 for instance).
						# Here we're overriding the smallest possible method to provide
						# a clean sequence without making the same assumption.

						A__							:					int       =	[]
						for i in range(len(A__    )    ):
							try:
								A__							:					List[Any]       =	tokenizer.decode([i]					, clean_up_tokenization_spaces=A__    )
							except UnicodeDecodeError:
								pass
							toks.append((i, tok)    )

						A__							:					Dict       =	list(filter(lambda A__    : re.match(r"""^[ a-zA-Z]+$"""					, t[1]    )					, A__    )    )
						A__							:					str       =	list(filter(lambda A__    : [t[0]] == tokenizer.encode(t[1]					, add_special_tokens=A__    )					, A__    )    )
						if max_length is not None and len(A__    ) > max_length:
							A__							:					List[Any]       =	toks[:max_length]
						if min_length is not None and len(A__    ) < min_length and len(A__    ) > 0:
							while len(A__    ) < min_length:
								A__							:					List[Any]       =	toks + toks
        # toks_str = [t[1] for t in toks]
						A__							:					Tuple       =	[t[0] for t in toks]

						# Ensure consistency
						A__							:					Union[str, Any]       =	tokenizer.decode(A__					, clean_up_tokenization_spaces=A__    )
						if " " not in output_txt and len(A__    ) > 1:
							A__							:					List[Any]       =	(
							    tokenizer.decode([toks_ids[0]]					, clean_up_tokenization_spaces=A__    )
							    + """ """
							    + tokenizer.decode(toks_ids[1:]					, clean_up_tokenization_spaces=A__    )
							)
						if with_prefix_space:
							A__							:					List[Any]       =	""" """ + output_txt
						A__							:					Optional[Any]       =	tokenizer.encode(A__					, add_special_tokens=A__    )
						return output_txt, output_ids






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	self.perceiver_tokenizer
						A__							:					str       =	"""Unicode €."""
						A__							:					Union[str, Any]       =	tokenizer(A__    )
						A__							:					Any       =	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
						self.assertEqual(encoded["""input_ids"""]					, A__    )

						# decoding
						A__							:					List[Any]       =	tokenizer.decode(A__    )
						self.assertEqual(A__					, """[CLS]Unicode €.[SEP]"""    )

						A__							:					Any       =	tokenizer("""e è é ê ë"""    )
						A__							:					str       =	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
						self.assertEqual(encoded["""input_ids"""]					, A__    )
						# decoding
						A__							:					Tuple       =	tokenizer.decode(A__    )
						self.assertEqual(A__					, """[CLS]e è é ê ë[SEP]"""    )

						# encode/decode, but with `encode` instead of `__call__`
						self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""    )    )					, """[CLS]e è é ê ë[SEP]"""    )






					def 						__A    (				self    ):
						A__							:					int       =	self.perceiver_tokenizer
						A__							:					Optional[int]       =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						# fmt: off
						A__							:					str       =	[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
						# fmt: on
						A__							:					Union[str, Any]       =	tokenizer(A__					, padding=A__					, return_tensors=A__    )
						self.assertIsInstance(A__					, A__    )

						if FRAMEWORK != "jax":
							A__							:					Dict       =	list(batch.input_ids.numpy()[0]    )
						else:
							A__							:					List[Any]       =	list(batch.input_ids.tolist()[0]    )

						self.assertListEqual(A__					, A__    )

						self.assertEqual((2, 38)					, batch.input_ids.shape    )
						self.assertEqual((2, 38)					, batch.attention_mask.shape    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.perceiver_tokenizer
						A__							:					Tuple       =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						A__							:					List[str]       =	tokenizer(A__					, padding=A__					, return_tensors=A__    )
						# check if input_ids are returned and no decoder_input_ids
						self.assertIn("""input_ids"""					, A__    )
						self.assertIn("""attention_mask"""					, A__    )
						self.assertNotIn("""decoder_input_ids"""					, A__    )
						self.assertNotIn("""decoder_attention_mask"""					, A__    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	self.perceiver_tokenizer
						A__							:					Optional[Any]       =	[
						    """Summary of the text.""",
						    """Another summary.""",
						]
						A__							:					Optional[Any]       =	tokenizer(
						    text_target=A__					, max_length=32					, padding="""max_length"""					, truncation=A__					, return_tensors=A__    )
						self.assertEqual(32					, targets["""input_ids"""].shape[1]    )






					def 						__A    (				self    ):
						# safety check on max_len default value so we are sure the test works
						A__							:					List[str]       =	self.get_tokenizers()
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
								self.assertNotEqual(tokenizer.model_max_length					, 42    )

        # Now let's start the test
						A__							:					Optional[int]       =	self.get_tokenizers()
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
								# Isolate this from the other tests because we save additional tokens/etc
								A__							:					Tuple       =	tempfile.mkdtemp()

								A__							:					Tuple       =	""" He is very happy, UNwant\u00E9d,running"""
								A__							:					Optional[Any]       =	tokenizer.encode(A__					, add_special_tokens=A__    )
								tokenizer.save_pretrained(A__    )

								A__							:					List[str]       =	tokenizer.__class__.from_pretrained(A__    )
								A__							:					int       =	after_tokenizer.encode(A__					, add_special_tokens=A__    )
								self.assertListEqual(A__					, A__    )

								shutil.rmtree(A__    )

						A__							:					List[str]       =	self.get_tokenizers(model_max_length=42    )
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
								# Isolate this from the other tests because we save additional tokens/etc
								A__							:					Optional[Any]       =	tempfile.mkdtemp()

								A__							:					Any       =	""" He is very happy, UNwant\u00E9d,running"""
								tokenizer.add_tokens(["""bim""", """bambam"""]    )
								A__							:					Optional[Any]       =	tokenizer.additional_special_tokens
								additional_special_tokens.append("""new_additional_special_token"""    )
								tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}    )
								A__							:					Optional[Any]       =	tokenizer.encode(A__					, add_special_tokens=A__    )
								tokenizer.save_pretrained(A__    )

								A__							:					List[Any]       =	tokenizer.__class__.from_pretrained(A__    )
								A__							:					Optional[int]       =	after_tokenizer.encode(A__					, add_special_tokens=A__    )
								self.assertListEqual(A__					, A__    )
								self.assertIn("""new_additional_special_token"""					, after_tokenizer.additional_special_tokens    )
								self.assertEqual(after_tokenizer.model_max_length					, 42    )

								A__							:					Dict       =	tokenizer.__class__.from_pretrained(A__					, model_max_length=43    )
								self.assertEqual(tokenizer.model_max_length					, 43    )

								shutil.rmtree(A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	[]
						if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

						if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

						for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
								tokenizer_utils.save_pretrained(A__    )

								with open(os.path.join(A__					, """special_tokens_map.json"""    )					, encoding="""utf-8"""    ) as json_file:
									A__							:					int       =	json.load(A__    )

								with open(os.path.join(A__					, """tokenizer_config.json"""    )					, encoding="""utf-8"""    ) as json_file:
									A__							:					Tuple       =	json.load(A__    )

								A__							:					str       =	[F"""<extra_id_{i}>""" for i in range(125    )]

								A__							:					Optional[Any]       =	added_tokens_extra_ids + [
								    """an_additional_special_token"""
								]
								A__							:					Dict       =	added_tokens_extra_ids + [
								    """an_additional_special_token"""
								]

								with open(os.path.join(A__					, """special_tokens_map.json"""    )					, """w"""					, encoding="""utf-8"""    ) as outfile:
									json.dump(A__					, A__    )
								with open(os.path.join(A__					, """tokenizer_config.json"""    )					, """w"""					, encoding="""utf-8"""    ) as outfile:
									json.dump(A__					, A__    )

								# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
								# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
								# "special_tokens_map.json" files
								A__							:					List[str]       =	tokenizer_class.from_pretrained(
								    A__					, )
								self.assertIn(
								    """an_additional_special_token"""					, tokenizer_without_change_in_init.additional_special_tokens    )
								self.assertEqual(
								    ["""an_additional_special_token"""]					, tokenizer_without_change_in_init.convert_ids_to_tokens(
								        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]    )    )					, )

								# Now we test that we can change the value of additional_special_tokens in the from_pretrained
								A__							:					Tuple       =	added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""					, lstrip=A__    )]
								A__							:					Tuple       =	tokenizer_class.from_pretrained(
								    A__					, additional_special_tokens=A__					, )

								self.assertIn("""a_new_additional_special_token"""					, tokenizer.additional_special_tokens    )
								self.assertEqual(
								    ["""a_new_additional_special_token"""]					, tokenizer.convert_ids_to_tokens(
								        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]    )    )					, )






					def 						__A    (				self    ):
						A__							:					Any       =	self.perceiver_tokenizer
						self.assertEqual(tokenizer.decode([178]    )					, """�"""    )






					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
						# strings and special added tokens as tokens
						A__							:					int       =	self.get_tokenizers(fast=A__					, do_lower_case=A__    )
						for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""    ):
								A__							:					Tuple       =	["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
								A__							:					Dict       =	tokenizer.convert_tokens_to_string(A__    )

								self.assertIsInstance(A__					, A__    )






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      (lowercase_:				List[str]					,   lowercase_:				str      )  ->   Optional[Any]:
	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		A__							:					Union[str, Any]       =	flax_key_tuple[:-1] + ("""weight""",)
		A__							:					Optional[int]       =	torch.permute(lowercase_					,   (0, 2, 1)      )
	elif flax_key_tuple[-1] == "kernel" and ".".join(lowercase_      ):
		# linear layer
		A__							:					Optional[Any]       =	flax_key_tuple[:-1] + ("""weight""",)
		A__							:					int       =	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      (lowercase_:				Tuple					,   lowercase_:				Optional[int]					,   lowercase_:				str      )  ->   Union[str, Any]:
	if "metadata" in layer:
		A__							:					Tuple       =	layer.split("""metadata"""      )
		A__							:					Optional[Any]       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[Any]       =	[tuple(("""metadata""" + split_layer[1]).split("""/"""      )      )]
	elif "kvstore" in layer:
		A__							:					str       =	layer.split("""kvstore"""      )
		A__							:					int       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[int]       =	[tuple(("""kvstore""" + split_layer[1]).split("""/"""      )      )]

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

	if "kvstore/path" in layer:
		A__							:					Dict       =	f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
	elif "kvstore/driver" in layer:
		A__							:					Optional[int]       =	"""file"""
	else:
		A__							:					str       =	checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				List[Any]      )  ->   int:
	A__							:					int       =	rename_keys(lowercase_      )
	A__							:					Any       =	{}
	for k, v in current_block.items():
		A__							:					Dict       =	v
	A__							:					str       =	new_current_block
	torch.save(lowercase_					,   lowercase_      )

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[int]					,   lowercase_:				str = WEIGHTS_NAME      )  ->   Tuple:
	A__							:					Optional[int]       =	convert_file_size_to_int(lowercase_      )
	A__							:					List[Any]       =	[]
	A__							:					int       =	{}
	A__							:					List[str]       =	0
	A__							:					Any       =	0

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

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

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

		# use the renaming pattern from the small conversion scripts
		A__     ,     A__							:					Any       =	rename_base_flax_keys(tuple(key.split("""/"""      )      )					,   lowercase_      )
		A__							:					Any       =	"""/""".join(lowercase_      )

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

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

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

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

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

    # Add the metadata
	A__							:					Tuple       =	{"""total_size""": total_size}
	A__							:					Union[str, Any]       =	{"""metadata""": metadata, """weight_map""": weight_map}

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

	return metadata, index


if __name__ == "__main__":
	A_ :				Optional[int]						 =    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_ :				Dict						 =    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      ()  ->   int:
	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

	A__							:					Union[str, Any]       =	tokenizer(lowercase_					,   return_tensors="""pt"""      ).input_ids
	A__							:					Tuple       =	model.generate(lowercase_					,   decoder_start_token_id=0      )
	print(tokenizer.decode(out[0]      )      )



