
"""simple docstring"""





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def       lowerCamelCase__		(			__snake_case	)							->		Optional[int]:


					"""simple docstring"""





					_UpperCamelCase							 =  os.path.join(args.tf_model_dir,     '''parameters.json'''	)
					_UpperCamelCase							 =  json.loads(open(__snake_case	).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(__snake_case	).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(__snake_case	)
															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(__snake_case	)
																				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(__snake_case	)
																				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(__snake_case	)
															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(__snake_case	)
																				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(__snake_case	)
																				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(__snake_case	)
																				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(__snake_case	)
															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(__snake_case	)
																				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(__snake_case	)
															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(__snake_case	)
																									_UpperCamelCase							 =  '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																									_UpperCamelCase							 =  torch.tensor(__snake_case	)
																									_UpperCamelCase							 =  '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																									_UpperCamelCase							 =  torch.tensor(__snake_case	)
																				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(__snake_case	)
															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(__snake_case	)
																				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(__snake_case	)
															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(__snake_case	)
																				if key_name.startswith('''model/wte'''	):
																									_UpperCamelCase							 =  '''lm_head.weight'''
																									_UpperCamelCase							 =  vnp.copy()  # same in embedded
																									_UpperCamelCase							 =  torch.tensor(__snake_case	)
															elif key_name.startswith('''model/wob'''	):
																				_UpperCamelCase							 =  '''final_logits_bias'''
																				_UpperCamelCase							 =  vnp.copy()  # same in embedded
																				_UpperCamelCase							 =  state.reshape((1, -1)	)
																				_UpperCamelCase							 =  torch.tensor(__snake_case	)
															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(__snake_case	)
															elif key_name == "model/dense_1/bias":
																				_UpperCamelCase							 =  '''model.last_project.bias'''
																				_UpperCamelCase							 =  vnp.copy()  # same because it is one dimensional
																				_UpperCamelCase							 =  torch.tensor(__snake_case	)
					torch.save(__snake_case,     args.output	)


if __name__ == "__main__":
				_a														=		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""")
				_a														=		parser.parse_args()
				convert_tf_gptsan_to_pt(args)








"""simple docstring"""





import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_a														=		"""http://www.mocksite.com/file1.txt"""
_a														=		"""\"text\": [\"foo\", \"foo\"]"""
_a														=		"""6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"""






class     _UpperCAmelCase:
		lowercase__						=      2_00
		lowercase__						=      {'Content-Length': '100'}
		lowercase__						=      {}



		def 						UpperCAmelCase							(					self				,		**__a)       ->       Optional[int]:






							'''simple docstring'''
							return [bytes(__a				,		'''utf-8''')]





def       lowerCamelCase__		(			*__snake_case,     **__snake_case	)							->		int:


					"""simple docstring"""





					return MockResponse()



@pytest.mark.parametrize('''urls_type''',     [str, list, dict]	)
def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		Dict:


					"""simple docstring"""





					import requests

					monkeypatch.setattr(__snake_case,     '''request''',     __snake_case	)

					_UpperCamelCase							 =  URL
					if issubclass(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  url
					elif issubclass(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  [url]
					elif issubclass(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  {'''train''': url}
					_UpperCamelCase							 =  '''dummy'''
					_UpperCamelCase							 =  '''downloads'''
					_UpperCamelCase							 =  tmp_path
					_UpperCamelCase							 =  DownloadConfig(
					    cache_dir=os.path.join(__snake_case,     __snake_case	),     use_etag=__snake_case,     )
					_UpperCamelCase							 =  DownloadManager(dataset_name=__snake_case,     download_config=__snake_case	)
					_UpperCamelCase							 =  dl_manager.download(__snake_case	)
					_UpperCamelCase							 =  urls
					for downloaded_paths in [downloaded_paths]:
										if isinstance(__snake_case,     __snake_case	):
															_UpperCamelCase							 =  [downloaded_paths]
															_UpperCamelCase							 =  [urls]
										elif isinstance(__snake_case,     __snake_case	):
															assert "train" in downloaded_paths.keys()
															_UpperCamelCase							 =  downloaded_paths.values()
															_UpperCamelCase							 =  urls.values()
										assert downloaded_paths
										for downloaded_path, input_url in zip(__snake_case,     __snake_case	):
															assert downloaded_path == dl_manager.downloaded_paths[input_url]
															_UpperCamelCase							 =  Path(__snake_case	)
															_UpperCamelCase							 =  downloaded_path.parts
															assert parts[-1] == HASH
															assert parts[-2] == cache_subdir
															assert downloaded_path.exists()
															_UpperCamelCase							 =  downloaded_path.read_text()
															assert content == CONTENT
															_UpperCamelCase							 =  downloaded_path.with_suffix('''.json'''	)
															assert metadata_downloaded_path.exists()
															_UpperCamelCase							 =  json.loads(metadata_downloaded_path.read_text()	)
															assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('''paths_type''',     [str, list, dict]	)
def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  str(__snake_case	)
					if issubclass(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  filename
					elif issubclass(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  [filename]
					elif issubclass(__snake_case,     __snake_case	):
										_UpperCamelCase							 =  {'''train''': filename}
					_UpperCamelCase							 =  '''dummy'''
					_UpperCamelCase							 =  xz_file.parent
					_UpperCamelCase							 =  '''extracted'''
					_UpperCamelCase							 =  DownloadConfig(
					    cache_dir=__snake_case,     use_etag=__snake_case,     )
					_UpperCamelCase							 =  DownloadManager(dataset_name=__snake_case,     download_config=__snake_case	)
					_UpperCamelCase							 =  dl_manager.extract(__snake_case	)
					_UpperCamelCase							 =  paths
					for extracted_paths in [extracted_paths]:
										if isinstance(__snake_case,     __snake_case	):
															_UpperCamelCase							 =  [extracted_paths]
															_UpperCamelCase							 =  [paths]
										elif isinstance(__snake_case,     __snake_case	):
															assert "train" in extracted_paths.keys()
															_UpperCamelCase							 =  extracted_paths.values()
															_UpperCamelCase							 =  paths.values()
										assert extracted_paths
										for extracted_path, input_path in zip(__snake_case,     __snake_case	):
															assert extracted_path == dl_manager.extracted_paths[input_path]
															_UpperCamelCase							 =  Path(__snake_case	)
															_UpperCamelCase							 =  extracted_path.parts
															assert parts[-1] == hash_url_to_filename(__snake_case,     etag=__snake_case	)
															assert parts[-2] == extracted_subdir
															assert extracted_path.exists()
															_UpperCamelCase							 =  extracted_path.read_text()
															_UpperCamelCase							 =  text_file.read_text()
															assert extracted_file_content == expected_file_content



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[Any]:


					"""simple docstring"""





					assert path.endswith('''.jsonl'''	)
					for num_items, line in enumerate(__snake_case,     start=1	):
										_UpperCamelCase							 =  json.loads(line.decode('''utf-8'''	)	)
										assert item.keys() == {"col_1", "col_2", "col_3"}
					assert num_items == 4



@pytest.mark.parametrize('''archive_jsonl''',     ['''tar_jsonl_path''', '''zip_jsonl_path''']	)
def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  request.getfixturevalue(__snake_case	)
					_UpperCamelCase							 =  DownloadManager()
					for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__snake_case	),     start=1	):
										_test_jsonl(__snake_case,     __snake_case	)
					assert num_jsonl == 2



@pytest.mark.parametrize('''archive_nested_jsonl''',     ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']	)
def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  request.getfixturevalue(__snake_case	)
					_UpperCamelCase							 =  DownloadManager()
					for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__snake_case	),     start=1	):
										for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__snake_case	),     start=1	):
															_test_jsonl(__snake_case,     __snake_case	)
					assert num_tar == 1
					assert num_jsonl == 2



def       lowerCamelCase__		(			__snake_case	)							->		Dict:


					"""simple docstring"""





					_UpperCamelCase							 =  DownloadManager()
					for num_file, file in enumerate(dl_manager.iter_files(__snake_case	),     start=1	):
										assert os.path.basename(__snake_case	) == ("test.txt" if num_file == 1 else "train.txt")
					assert num_file == 2






