

import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_ :				Tuple						 =    pytest.mark.integration


A_ :				Union[str, Any]						 =    {'comet'}
A_ :				Tuple						 =    importlib.util.find_spec('fairseq') is not None

A_ :				Optional[int]						 =    {'code_eval'}
A_ :				Union[str, Any]						 =    os.name == 'nt'

A_ :				int						 =    {'bertscore', 'frugalscore', 'perplexity'}
A_ :				str						 =    importlib.util.find_spec('transformers') is not None

def 		UpperCamelCase      (lowercase_:				str      )  ->   List[str]:
	@wraps(lowercase_      )
	def wrapper(self:				int					,   lowercase_:				List[str]      ):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest("""\"test requires Fairseq\""""      )
		else:
			test_case(self					,   lowercase_      )

	return wrapper

def 		UpperCamelCase      (lowercase_:				str      )  ->   Optional[int]:
	@wraps(lowercase_      )
	def wrapper(self:				Optional[Any]					,   lowercase_:				Dict      ):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest("""\"test requires transformers\""""      )
		else:
			test_case(self					,   lowercase_      )

	return wrapper

def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   Tuple:
	@wraps(lowercase_      )
	def wrapper(self:				List[str]					,   lowercase_:				List[Any]      ):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest("""\"test not supported on Windows\""""      )
		else:
			test_case(self					,   lowercase_      )

	return wrapper

def 		UpperCamelCase      ()  ->   Optional[Any]:
	A__							:					List[str]       =	[metric_dir.split(os.sep      )[-2] for metric_dir in glob.glob("""./metrics/*/"""      )]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished



@parameterized.named_parameters(get_local_metric_names()       )
@for_all_test_methods(
    __magic_name__	,     __magic_name__	,     __magic_name__       )
@local
class 					_a		(parameterized.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Optional[Any]													= {}
					UpperCAmelCase__:     Any													= None






					@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
					@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""    )
					def 						__A    (				self					, A__    ):
						A__							:					int       =	"""[...]"""
						A__							:					Union[str, Any]       =	importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""					, A__    )    ).module_path    )
						A__							:					Optional[int]       =	datasets.load.import_main_class(metric_module.__name__					, dataset=A__    )
						# check parameters
						A__							:					int       =	inspect.signature(metric._compute    ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()    )    )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(A__					, metric_module.__name__    ):
							with self.use_local_metrics():
								try:
									A__							:					Optional[Any]       =	doctest.testmod(A__					, verbose=A__					, raise_on_error=A__    )
								except doctest.UnexpectedException as e:
									raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed					, 0    )
						self.assertGreater(results.attempted					, 1    )






					@slow
					def 						__A    (				self					, A__    ):
						A__							:					str       =	"""[...]"""
						A__							:					List[str]       =	importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""					, A__    )    ).module_path    )
						# run doctest
						with self.use_local_metrics():
							A__							:					int       =	doctest.testmod(A__					, verbose=A__					, raise_on_error=A__    )
						self.assertEqual(results.failed					, 0    )
						self.assertGreater(results.attempted					, 1    )






					@contextmanager
					def 						__A    (				self					, A__					, A__    ):
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
							with self.INTENSIVE_CALLS_PATCHER[metric_name](A__    ):
								yield
						else:
							yield






					@contextmanager
					def 						__A    (				self    ):
						def load_local_metric(A__					, *A__					, **A__    ):
							return load_metric(os.path.join("""metrics"""					, A__    )					, *A__					, **A__    )

						with patch("""datasets.load_metric"""    ) as mock_load_metric:
							A__							:					Optional[Any]       =	load_local_metric
							yield






					@classmethod
					def 						__A    (				cls					, A__    ):
						def wrapper(A__    ):
							A__							:					Any       =	contextmanager(A__    )
							A__							:					int       =	patcher
							return patcher

						return wrapper



@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""      )
def 		UpperCamelCase      (lowercase_:				Optional[Any]      )  ->   int:
	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string("""sv"""					,   """"""					,   """"""      )  # handle pytest cli flags



	class 					_a		(__magic_name__       ):


						'''simple docstring'''






						def 						__A    (				self					, A__    ):
							assert len(input_dict["""input_ids"""]    ) == 2
							return np.array([1.0_3, 1.0_4]    )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch("""bleurt.score._create_predictor"""      ) as mock_create_predictor:
		A__							:					List[Any]       =	MockedPredictor()
		yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""      )
def 		UpperCamelCase      (lowercase_:				List[Any]      )  ->   List[Any]:
	import torch

	def bert_cos_score_idf(lowercase_:				Tuple					,   lowercase_:				Union[str, Any]					,   *lowercase_:				Union[str, Any]					,   **lowercase_:				List[str]      ):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase_      )      )

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch("""bert_score.scorer.get_model"""      ), patch(
	    """bert_score.scorer.bert_cos_score_idf"""      ) as mock_bert_cos_score_idf:
		A__							:					Union[str, Any]       =	bert_cos_score_idf
		yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""      )
def 		UpperCamelCase      (lowercase_:				str      )  ->   Optional[Any]:
	def load_from_checkpoint(lowercase_:				List[Any]      ):



		class 					_a		:


							'''simple docstring'''






							def 						__A    (				self					, A__					, *A__					, **A__    ):
								assert len(A__    ) == 2
								A__							:					str       =	[0.1_9, 0.9_2]
								return scores, sum(A__    ) / len(A__    )



		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch("""comet.download_model"""      ) as mock_download_model:
		A__							:					Optional[Any]       =	None
		with patch("""comet.load_from_checkpoint"""      ) as mock_load_from_checkpoint:
			A__							:					Optional[Any]       =	load_from_checkpoint
			yield


def 		UpperCamelCase      ()  ->   List[str]:
	A__							:					List[Any]       =	load_metric(os.path.join("""metrics"""					,   """seqeval"""      )      )
	A__							:					List[Any]       =	"""ERROR"""
	A__							:					List[str]       =	f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
	with pytest.raises(lowercase_					,   match=re.escape(lowercase_      )      ):
		metric.compute(predictions=[]					,   references=[]					,   scheme=lowercase_      )






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]      )      )



