

import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import BertGenerationDecoder, BertGenerationEncoder



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=7					, A__=True					, A__=True					, A__=99					, A__=32					, A__=5					, A__=4					, A__=37					, A__="gelu"					, A__=0.1					, A__=0.1					, A__=50					, A__=0.0_2					, A__=True					, A__=None					, ):
						A__							:					Optional[Any]       =	parent
						A__							:					Optional[Any]       =	batch_size
						A__							:					Any       =	seq_length
						A__							:					Any       =	is_training
						A__							:					List[Any]       =	use_input_mask
						A__							:					int       =	vocab_size
						A__							:					int       =	hidden_size
						A__							:					Union[str, Any]       =	num_hidden_layers
						A__							:					Dict       =	num_attention_heads
						A__							:					Optional[int]       =	intermediate_size
						A__							:					Any       =	hidden_act
						A__							:					Any       =	hidden_dropout_prob
						A__							:					Optional[Any]       =	attention_probs_dropout_prob
						A__							:					int       =	max_position_embeddings
						A__							:					Optional[int]       =	initializer_range
						A__							:					Optional[int]       =	use_labels
						A__							:					List[Any]       =	scope






					def 						__A    (				self    ):
						A__							:					Any       =	ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size    )

						A__							:					Union[str, Any]       =	None
						if self.use_input_mask:
							A__							:					Optional[Any]       =	random_attention_mask([self.batch_size, self.seq_length]    )

						if self.use_labels:
							A__							:					List[Any]       =	ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size    )

						A__							:					Any       =	self.get_config()

						return config, input_ids, input_mask, token_labels






					def 						__A    (				self    ):
						return BertGenerationConfig(
						    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					, is_decoder=A__					, initializer_range=self.initializer_range					, )






					def 						__A    (				self    ):
						(
						    (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     
						)							:					List[str]       =	self.prepare_config_and_inputs()

						A__							:					List[str]       =	True
						A__							:					str       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
						A__							:					Optional[int]       =	ids_tensor([self.batch_size, self.seq_length]					, vocab_size=2    )

						return (
						    config,
						    input_ids,
						    input_mask,
						    token_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)






					def 						__A    (				self					, A__					, A__					, A__					, A__					, **A__					, ):
						A__							:					List[str]       =	BertGenerationEncoder(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Optional[int]       =	model(A__					, attention_mask=A__    )
						A__							:					List[str]       =	model(A__    )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, **A__					, ):
						A__							:					List[str]       =	True
						A__							:					List[Any]       =	BertGenerationEncoder(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					List[str]       =	model(
						    A__					, attention_mask=A__					, encoder_hidden_states=A__					, encoder_attention_mask=A__					, )
						A__							:					Any       =	model(
						    A__					, attention_mask=A__					, encoder_hidden_states=A__					, )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, A__					, A__					, **A__					, ):
						A__							:					str       =	True
						A__							:					Dict       =	True
						A__							:					str       =	BertGenerationDecoder(config=A__    ).to(A__    ).eval()

						# first forward pass
						A__							:					Any       =	model(
						    A__					, attention_mask=A__					, encoder_hidden_states=A__					, encoder_attention_mask=A__					, use_cache=A__					, )
						A__							:					List[Any]       =	outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						A__							:					int       =	ids_tensor((self.batch_size, 3)					, config.vocab_size    )
						A__							:					List[Any]       =	ids_tensor((self.batch_size, 3)					, vocab_size=2    )

						# append to next input_ids and
						A__							:					Union[str, Any]       =	torch.cat([input_ids, next_tokens]					, dim=-1    )
						A__							:					Dict       =	torch.cat([input_mask, next_mask]					, dim=-1    )

						A__							:					Optional[int]       =	model(
						    A__					, attention_mask=A__					, encoder_hidden_states=A__					, encoder_attention_mask=A__					, output_hidden_states=A__					, )["""hidden_states"""][0]
						A__							:					Dict       =	model(
						    A__					, attention_mask=A__					, encoder_hidden_states=A__					, encoder_attention_mask=A__					, past_key_values=A__					, output_hidden_states=A__					, )["""hidden_states"""][0]

						# select random slice
						A__							:					str       =	ids_tensor((1,)					, output_from_past.shape[-1]    ).item()
						A__							:					int       =	output_from_no_past[:, -3:, random_slice_idx].detach()
						A__							:					Dict       =	output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(A__					, A__					, atol=1e-3    )    )






					def 						__A    (				self					, A__					, A__					, A__					, A__					, *A__					, ):
						A__							:					Dict       =	BertGenerationDecoder(A__    )
						model.to(A__    )
						model.eval()
						A__							:					Tuple       =	model(A__					, attention_mask=A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.seq_length, self.vocab_size)    )






					def 						__A    (				self    ):
						A__     ,     A__     ,     A__     ,     A__							:					str       =	self.prepare_config_and_inputs()
						A__							:					Dict       =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_torch
class 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Union[str, Any]													= (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
					UpperCAmelCase__:     Union[str, Any]													= (BertGenerationDecoder,) if is_torch_available() else ()
					UpperCAmelCase__:     Optional[Any]													= (
					    {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
					    if is_torch_available()
					    else {}
					)






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	BertGenerationEncoderTester(self    )
						A__							:					str       =	ConfigTester(self					, config_class=A__					, hidden_size=37    )






					def 						__A    (				self    ):
						self.config_tester.run_common_tests()






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__     ,     A__     ,     A__     ,     A__							:					Dict       =	self.model_tester.prepare_config_and_inputs()
						A__							:					int       =	"""bert"""
						self.model_tester.create_and_check_model(A__					, A__					, A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__    )






					def 						__A    (				self    ):
						# This regression test was failing with PyTorch < 1.3
						(
						    (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     (
						    A__
						)     ,     
						)							:					Union[str, Any]       =	self.model_tester.prepare_config_and_inputs_for_decoder()

						A__							:					List[str]       =	None

						self.model_tester.create_and_check_model_as_decoder(
						    A__					, A__					, A__					, A__					, A__					, A__					, )






					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_for_causal_lm(*A__    )






					@slow
					def 						__A    (				self    ):
						A__							:					int       =	BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""    )
						self.assertIsNotNone(A__    )



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					@slow
					def 						__A    (				self    ):
						A__							:					Tuple       =	BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""    )
						A__							:					Optional[Any]       =	torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]    )
						with torch.no_grad():
							A__							:					Union[str, Any]       =	model(A__    )[0]
						A__							:					str       =	torch.Size([1, 8, 1024]    )
						self.assertEqual(output.shape					, A__    )
						A__							:					Tuple       =	torch.tensor(
						    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]    )
						self.assertTrue(torch.allclose(output[:, :3, :3]					, A__					, atol=1e-4    )    )



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					@slow
					def 						__A    (				self    ):
						A__							:					Optional[int]       =	BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""    )
						A__							:					Union[str, Any]       =	torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]    )
						with torch.no_grad():
							A__							:					str       =	model(A__    )[0]
						A__							:					str       =	torch.Size([1, 8, 5_0358]    )
						self.assertEqual(output.shape					, A__    )
						A__							:					Optional[Any]       =	torch.tensor(
						    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]    )
						self.assertTrue(torch.allclose(output[:, :3, :3]					, A__					, atol=1e-4    )    )






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]      )      )



