'''simple docstring'''




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam






class 							a__(   datasets.BeamBasedBuilder      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					return datasets.DatasetInfo(
					    features=datasets.Features({"""content""": datasets.Value("""string""")})			,       supervised_keys=__lowerCAmelCase			,       )



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,       gen_kwargs={"""examples""": get_test_dummy_examples()})]







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					import apache_beam as beam

					return pipeline | "Load Examples" >> beam.Create(__lowerCAmelCase)







class 							a__(   datasets.BeamBasedBuilder      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					return datasets.DatasetInfo(
					    features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""")})})			,       supervised_keys=__lowerCAmelCase			,       )



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					return [
					    datasets.SplitGenerator(name=datasets.Split.TRAIN			,       gen_kwargs={"""examples""": get_test_nested_examples()})
					]







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					import apache_beam as beam

					return pipeline | "Load Examples" >> beam.Create(__lowerCAmelCase)







def 							snake_case__				(     )       ->      int:

		'''simple docstring'''


		return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]					)]






def 							snake_case__				(     )       ->      List[str]:

		'''simple docstring'''


		return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]					)]






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			@require_beam
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    len(get_test_dummy_examples())
					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							lowerCAmelCase     =    DummyBeamDataset(cache_dir=__lowerCAmelCase			,       beam_runner="""DirectRunner""")
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(__lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       f"{builder.name}-train.arrow")))
							self.assertDictEqual(builder.info.features			,       datasets.Features({"""content""": datasets.Value("""string""")}))
							lowerCAmelCase     =    builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows			,       __lowerCAmelCase)
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples			,       __lowerCAmelCase)
							self.assertDictEqual(dset["""train"""][0]			,       get_test_dummy_examples()[0][1])
							self.assertDictEqual(
							    dset["""train"""][expected_num_examples - 1]			,       get_test_dummy_examples()[expected_num_examples - 1][1])
							self.assertTrue(
							    os.path.exists(os.path.join(__lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       """dataset_info.json""")))
							del dset



			@require_beam
			def 				a_			(  self):




					"""simple docstring"""


					import apache_beam as beam

					lowerCAmelCase     =    beam.io.parquetio.WriteToParquet

					lowerCAmelCase     =    len(get_test_dummy_examples())
					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							lowerCAmelCase     =    DummyBeamDataset(cache_dir=__lowerCAmelCase			,       beam_runner="""DirectRunner""")
							with patch("""apache_beam.io.parquetio.WriteToParquet""") as write_parquet_mock:
									lowerCAmelCase     =    partial(__lowerCAmelCase			,       num_shards=2)
									builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            __lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       f"{builder.name}-train-00000-of-00002.arrow")))
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            __lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       f"{builder.name}-train-00000-of-00002.arrow")))
							self.assertDictEqual(builder.info.features			,       datasets.Features({"""content""": datasets.Value("""string""")}))
							lowerCAmelCase     =    builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows			,       __lowerCAmelCase)
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples			,       __lowerCAmelCase)
							# Order is not preserved when sharding, so we just check that all the elements are there
							self.assertListEqual(sorted(dset["""train"""]["""content"""])			,       sorted(["""foo""", """bar""", """foobar"""]))
							self.assertTrue(
							    os.path.exists(os.path.join(__lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       """dataset_info.json""")))
							del dset



			@require_beam
			def 				a_			(  self):




					"""simple docstring"""


					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							lowerCAmelCase     =    DummyBeamDataset(cache_dir=__lowerCAmelCase)
							self.assertRaises(datasets.builder.MissingBeamOptions			,       builder.download_and_prepare)







			@require_beam
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    len(get_test_nested_examples())
					with tempfile.TemporaryDirectory() as tmp_cache_dir:
							lowerCAmelCase     =    NestedBeamDataset(cache_dir=__lowerCAmelCase			,       beam_runner="""DirectRunner""")
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(__lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       f"{builder.name}-train.arrow")))
							self.assertDictEqual(
							    builder.info.features			,       datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""")})}))
							lowerCAmelCase     =    builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows			,       __lowerCAmelCase)
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples			,       __lowerCAmelCase)
							self.assertDictEqual(dset["""train"""][0]			,       get_test_nested_examples()[0][1])
							self.assertDictEqual(
							    dset["""train"""][expected_num_examples - 1]			,       get_test_nested_examples()[expected_num_examples - 1][1])
							self.assertTrue(
							    os.path.exists(os.path.join(__lowerCAmelCase			,       builder.name			,       """default"""			,       """0.0.0"""			,       """dataset_info.json""")))
							del dset





'''simple docstring'''




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 							snake_case__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    os.path.join(args.tf_model_dir ,  """parameters.json"""					)
		lowerCAmelCase     =    json.loads(open(_A					).read()					)
		if not params:
				raise ValueError(
				    f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."					)
		if not args.output.endswith(""".pt"""					):
				lowerCAmelCase     =    args.output + """.pt"""
		lowerCAmelCase     =    OrderedDict()
		with tf.device("""/CPU:0"""					):
				lowerCAmelCase     =    tf.train.load_checkpoint(args.tf_model_dir					)
				lowerCAmelCase     =    reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						lowerCAmelCase     =    reader.get_tensor(_A					).astype(np.floataa					)
						if key_name.endswith("""/adam_m"""					) or key_name.endswith("""/adam_v"""					):
								continue
						if key_name.startswith("""pasts/"""					):
								if key_name.startswith("""pasts/mlp"""					):
										lowerCAmelCase     =    int(key_name[9]					)
								elif key_name.startswith("""pasts/out"""					):
										lowerCAmelCase     =    8
								lowerCAmelCase     =    """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/moe"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/switch_gating/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/softmlp/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/wo/kernel"""					) or key_name.endswith("""/wi/kernel"""					):
										lowerCAmelCase     =    key_name[-9:-7]
										for i in range(16					):
												lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
												lowerCAmelCase     =    (
												    vnp[i].transpose([1, 0]					).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/mlp"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/p1/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p1/bias"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p2/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p2/bias"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/ln"""					):
								lowerCAmelCase     =    int(key_name[8:].split("""/"""					)[0]					)
								if key_name.endswith("""/b"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.norm.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/g"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.norm.weight""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/att"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/qkv/kernel"""					):
										lowerCAmelCase     =    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										lowerCAmelCase     =    state[:, 0, :, :]
										lowerCAmelCase     =    state[:, 1, :, :]
										lowerCAmelCase     =    state[:, 2, :, :]
										lowerCAmelCase     =    (
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]					)
										    .transpose([1, 0]					)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    (
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]					)
										    .transpose([1, 0]					)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    (
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]					)
										    .transpose([1, 0]					)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/o/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
										lowerCAmelCase     =    (
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/an"""					):
								lowerCAmelCase     =    int(key_name[8:].split("""/"""					)[0]					)
								if key_name.endswith("""/b"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.norm.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/g"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.norm.weight""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif (
						    key_name.startswith("""model/wte"""					)
						    or key_name.startswith("""model/wpe"""					)
						    or key_name.startswith("""model/ete"""					)
						):
								lowerCAmelCase     =    {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
								    key_name[-3:]
								]
								lowerCAmelCase     =    """model.%s.weight""" % nlayer
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    torch.tensor(_A					)
								if key_name.startswith("""model/wte"""					):
										lowerCAmelCase     =    """lm_head.weight"""
										lowerCAmelCase     =    vnp.copy()  # same in embedded
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/wob"""					):
								lowerCAmelCase     =    """final_logits_bias"""
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    state.reshape((1, -1)					)
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name == "model/dense/kernel":
								lowerCAmelCase     =    """model.last_project.weight"""
								lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name == "model/dense_1/bias":
								lowerCAmelCase     =    """model.last_project.bias"""
								lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
								lowerCAmelCase     =    torch.tensor(_A					)
		torch.save(_A ,  args.output					)


if __name__ == "__main__":
						__lowercase        =    argparse.ArgumentParser(
						    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
						parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
						__lowercase        =    parser.parse_args()
						convert_tf_gptsan_to_pt(args)




