
'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       str:

				"""simple docstring"""







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


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      int          =			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')
					_SCREAMING_SNAKE_CASE    :      List[str]          =			parser.parse_args()
					convert_tf_gptsan_to_pt(args)

import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

_SCREAMING_SNAKE_CASE    :      Tuple          =			'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,			_KWARGS_DESCRIPTION   )
class  UpperCamelCase__		(    datasets.Metric   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					if version.parse(scb.__version__		) < version.parse('''1.4.12'''		):
									raise ImportWarning(
									    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
									    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''		)
					return datasets.MetricInfo(
					    description=_DESCRIPTION				,       citation=_CITATION				,       homepage='''http://www.cs.umd.edu/~snover/tercom/'''				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
					        {
					            '''predictions''': datasets.Value('''string'''				,       id='''sequence'''		),
					            '''references''': datasets.Sequence(datasets.Value('''string'''				,       id='''sequence'''		)				,       id='''references'''		),
					        }		)				,       codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter''']				,       reference_urls=[
					        '''https://github.com/jhclark/tercom''',
					    ]				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__ = False				,       UpperCamelCase__ = False				,       UpperCamelCase__ = False				,       UpperCamelCase__ = False				,       ):
					A__  :	List[Any]         =							len(references[0]		)
					if any(len(UpperCamelCase__		) != references_per_prediction for refs in references		):
									raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
					A__  :	Dict         =							[[refs[i] for refs in references] for i in range(UpperCamelCase__		)]

					A__  :	Optional[Any]         =							TER(
					    normalized=UpperCamelCase__				,       no_punct=UpperCamelCase__				,       asian_support=UpperCamelCase__				,       case_sensitive=UpperCamelCase__				,       )
					A__  :	str         =							sb_ter.corpus_score(UpperCamelCase__				,       UpperCamelCase__		)

					return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}