






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       assert isinstance(__UpperCamelCase	,      __UpperCamelCase		)
       assert dataset.num_rows == 4
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_1", "col_2", "col_3"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""	,      [False, True]		)
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      cache_dir=__UpperCamelCase	,      keep_in_memory=__UpperCamelCase		).read()
       _check_json_dataset(__UpperCamelCase	,      __UpperCamelCase		)






@pytest.mark.parametrize(
    """features"""	,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,      )
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       UpperCAmelCase	    =			features.copy() if features else default_expected_features
       UpperCAmelCase	    =			(
           Features({feature: Value(__UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
       )
       UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      features=__UpperCamelCase	,      cache_dir=__UpperCamelCase		).read()
       _check_json_dataset(__UpperCamelCase	,      __UpperCamelCase		)






@pytest.mark.parametrize(
    """features"""	,      [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]	,      )
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
       UpperCAmelCase	    =			features.copy() if features else default_expected_features
       UpperCAmelCase	    =			(
           Features({feature: Value(__UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
       )
       UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      features=__UpperCamelCase	,      cache_dir=__UpperCamelCase		).read()
       assert isinstance(__UpperCamelCase	,      __UpperCamelCase		)
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_3", "col_1", "col_2"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
       UpperCAmelCase	    =			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
       UpperCAmelCase	    =			features.copy()
       UpperCAmelCase	    =			(
           Features({feature: Value(__UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
       )
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      features=__UpperCamelCase	,      cache_dir=__UpperCamelCase		).read()
       assert isinstance(__UpperCamelCase	,      __UpperCamelCase		)
       assert dataset.num_rows == 2
       assert dataset.num_columns == 3
       assert dataset.column_names == ["col_2", "col_3", "col_1"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""split"""	,      [None, NamedSplit("""train"""		), """train""", """test"""]		)
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      cache_dir=__UpperCamelCase	,      split=__UpperCamelCase		).read()
       _check_json_dataset(__UpperCamelCase	,      __UpperCamelCase		)
       assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""	,      [str, list]		)
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       if issubclass(__UpperCamelCase	,      __UpperCamelCase		):
              UpperCAmelCase	    =			jsonl_path
       elif issubclass(__UpperCamelCase	,      __UpperCamelCase		):
              UpperCAmelCase	    =			[jsonl_path]
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      cache_dir=__UpperCamelCase		).read()
       _check_json_dataset(__UpperCamelCase	,      __UpperCamelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=("train",)		):







       '''simple docstring'''
       assert isinstance(__UpperCamelCase	,      __UpperCamelCase		)
       for split in splits:
              UpperCAmelCase	    =			dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 3
              assert dataset.column_names == ["col_1", "col_2", "col_3"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""	,      [False, True]		)
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              UpperCAmelCase	    =			JsonDatasetReader({"""train""": jsonl_path}	,      cache_dir=__UpperCamelCase	,      keep_in_memory=__UpperCamelCase		).read()
       _check_json_datasetdict(__UpperCamelCase	,      __UpperCamelCase		)






@pytest.mark.parametrize(
    """features"""	,      [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,      )
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       UpperCAmelCase	    =			features.copy() if features else default_expected_features
       UpperCAmelCase	    =			(
           Features({feature: Value(__UpperCamelCase		) for feature, dtype in features.items()}		) if features is not None else None
       )
       UpperCAmelCase	    =			JsonDatasetReader({"""train""": jsonl_path}	,      features=__UpperCamelCase	,      cache_dir=__UpperCamelCase		).read()
       _check_json_datasetdict(__UpperCamelCase	,      __UpperCamelCase		)






@pytest.mark.parametrize("""split"""	,      [None, NamedSplit("""train"""		), """train""", """test"""]		)
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       if split:
              UpperCAmelCase	    =			{split: jsonl_path}
       else:
              UpperCAmelCase	    =			"""train"""
              UpperCAmelCase	    =			{"""train""": jsonl_path, """test""": jsonl_path}
       UpperCAmelCase	    =			tmp_path / """cache"""
       UpperCAmelCase	    =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
       UpperCAmelCase	    =			JsonDatasetReader(__UpperCamelCase	,      cache_dir=__UpperCamelCase		).read()
       _check_json_datasetdict(__UpperCamelCase	,      __UpperCamelCase	,      splits=list(path.keys()		)		)
       assert all(dataset[split].split == split for split in path.keys()		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       return json.load(__UpperCamelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       return [json.loads(__UpperCamelCase		) for line in buffer]





class   UpperCamelCase_ :



  @pytest.mark.parametrize("""lines, load_json_function""" ,		[(True, load_json_lines), (False, load_json)]				)
  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Dict:



         """simple docstring"""





         with io.BytesIO() as buffer:
                JsonDatasetWriter(_snake_case ,		_snake_case ,		lines=_snake_case				).write()
                buffer.seek(0				)
                UpperCAmelCase	    =			load_json_function(_snake_case				)
         assert isinstance(_snake_case ,		_snake_case				)
         assert isinstance(exported_content[0] ,		_snake_case				)
         assert len(_snake_case				) == 10



  @pytest.mark.parametrize(
      """orient, container, keys, len_at""" ,		[
          ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
          ("""split""", dict, {"""columns""", """data"""}, """data"""),
          ("""index""", dict, set("""0123456789"""				), None),
          ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
          ("""values""", list, None, None),
          ("""table""", dict, {"""schema""", """data"""}, """data"""),
      ] ,		)
  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         with io.BytesIO() as buffer:
                JsonDatasetWriter(_snake_case ,		_snake_case ,		lines=_snake_case ,		orient=_snake_case				).write()
                buffer.seek(0				)
                UpperCAmelCase	    =			load_json(_snake_case				)
         assert isinstance(_snake_case ,		_snake_case				)
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(_snake_case ,		"""keys"""				) and not hasattr(exported_content[0] ,		"""keys"""				)
         if len_at:
                assert len(exported_content[len_at]				) == 10
         else:
                assert len(_snake_case				) == 10



  @pytest.mark.parametrize("""lines, load_json_function""" ,		[(True, load_json_lines), (False, load_json)]				)
  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         with io.BytesIO() as buffer:
                JsonDatasetWriter(_snake_case ,		_snake_case ,		lines=_snake_case ,		num_proc=2				).write()
                buffer.seek(0				)
                UpperCAmelCase	    =			load_json_function(_snake_case				)
         assert isinstance(_snake_case ,		_snake_case				)
         assert isinstance(exported_content[0] ,		_snake_case				)
         assert len(_snake_case				) == 10



  @pytest.mark.parametrize(
      """orient, container, keys, len_at""" ,		[
          ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
          ("""split""", dict, {"""columns""", """data"""}, """data"""),
          ("""index""", dict, set("""0123456789"""				), None),
          ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
          ("""values""", list, None, None),
          ("""table""", dict, {"""schema""", """data"""}, """data"""),
      ] ,		)
  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         with io.BytesIO() as buffer:
                JsonDatasetWriter(_snake_case ,		_snake_case ,		lines=_snake_case ,		orient=_snake_case ,		num_proc=2				).write()
                buffer.seek(0				)
                UpperCAmelCase	    =			load_json(_snake_case				)
         assert isinstance(_snake_case ,		_snake_case				)
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(_snake_case ,		"""keys"""				) and not hasattr(exported_content[0] ,		"""keys"""				)
         if len_at:
                assert len(exported_content[len_at]				) == 10
         else:
                assert len(_snake_case				) == 10



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         with pytest.raises(_snake_case				):
                with io.BytesIO() as buffer:
                       JsonDatasetWriter(_snake_case ,		_snake_case ,		num_proc=0				)



  @pytest.mark.parametrize("""compression, extension""" ,		[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]				)
  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			tmp_path_factory.mktemp("""data"""				) / f'''test.json.{extension}'''
         UpperCAmelCase	    =			str(shared_datadir / f'''test_file.json.{extension}'''				)
         JsonDatasetWriter(_snake_case ,		_snake_case ,		compression=_snake_case				).write()

         with fsspec.open(_snake_case ,		"""rb""" ,		compression="""infer"""				) as f:
                UpperCAmelCase	    =			f.read()
         with fsspec.open(_snake_case ,		"""rb""" ,		compression="""infer"""				) as f:
                UpperCAmelCase	    =			f.read()
         assert exported_content == original_content








"""simple docstring"""







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _lowerCAmelCase       (      lowerCAmelCase		):







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


if __name__ == "__main__":
 lowerCAmelCase_	:    Union[str, Any]            =      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''')
 lowerCAmelCase_	:    Any            =      parser.parse_args()
 convert_tf_gptsan_to_pt(args)
