


'''simple docstring'''



import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__A		=logging.getLogger(__name__)







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   Dict       				=       git.Repo(search_parent_directories=UpperCamelCase__   )
			UpperCAmelCase__					:   Any       				=       {
			    """repo_id""": str(UpperCamelCase__   ),
			    """repo_sha""": str(repo.head.object.hexsha   ),
			    """repo_branch""": str(repo.active_branch   ),
			}

			with open(os.path.join(UpperCamelCase__	, """git_log.json"""   )	, """w"""   ) as f:
						json.dump(UpperCamelCase__	, UpperCamelCase__	, indent=4   )







def       _UpperCamelCase (						UpperCamelCase__   ):
			if params.n_gpu <= 0:
						UpperCAmelCase__					:   List[str]       				=       0
						UpperCAmelCase__					:   Tuple       				=       -1
						UpperCAmelCase__					:   Tuple       				=       True
						UpperCAmelCase__					:   str       				=       False
						return

			assert torch.cuda.is_available()

			logger.info("""Initializing GPUs"""   )
			if params.n_gpu > 1:
						assert params.local_rank != -1

						UpperCAmelCase__					:   Dict       				=       int(os.environ["""WORLD_SIZE"""]   )
						UpperCAmelCase__					:   Optional[Any]       				=       int(os.environ["""N_GPU_NODE"""]   )
						UpperCAmelCase__					:   Any       				=       int(os.environ["""RANK"""]   )

						# number of nodes / node ID
						UpperCAmelCase__					:   str       				=       params.world_size // params.n_gpu_per_node
						UpperCAmelCase__					:   str       				=       params.global_rank // params.n_gpu_per_node
						UpperCAmelCase__					:   Dict       				=       True

						assert params.n_nodes == int(os.environ["""N_NODES"""]   )
						assert params.node_id == int(os.environ["""NODE_RANK"""]   )

			# local job (single GPU)
			else:
						assert params.local_rank == -1

						UpperCAmelCase__					:   Optional[Any]       				=       1
						UpperCAmelCase__					:   int       				=       0
						UpperCAmelCase__					:   Tuple       				=       0
						UpperCAmelCase__					:   int       				=       0
						UpperCAmelCase__					:   Tuple       				=       1
						UpperCAmelCase__					:   List[str]       				=       1
						UpperCAmelCase__					:   str       				=       False

			# sanity checks
			assert params.n_nodes >= 1
			assert 0 <= params.node_id < params.n_nodes
			assert 0 <= params.local_rank <= params.global_rank < params.world_size
			assert params.world_size == params.n_nodes * params.n_gpu_per_node

			# define whether this is the master process / if we are in multi-node distributed mode
			UpperCAmelCase__					:   Dict       				=       params.node_id == 0 and params.local_rank == 0
			UpperCAmelCase__					:   List[str]       				=       params.n_nodes > 1

			# summary
			UpperCAmelCase__					:   Tuple       				=       f'''--- Global rank: {params.global_rank} - '''
			logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes   )
			logger.info(PREFIX + """Node ID        : %i""" % params.node_id   )
			logger.info(PREFIX + """Local rank     : %i""" % params.local_rank   )
			logger.info(PREFIX + """World size     : %i""" % params.world_size   )
			logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node   )
			logger.info(PREFIX + """Master         : %s""" % str(params.is_master   )   )
			logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node   )   )
			logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu   )   )
			logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()   )

			# set GPU device
			torch.cuda.set_device(params.local_rank   )

			# initialize multi-GPU
			if params.multi_gpu:
						logger.info("""Initializing PyTorch distributed"""   )
						torch.distributed.init_process_group(
						    init_method="""env://"""	, backend="""nccl"""	, )







def       _UpperCamelCase (						UpperCamelCase__   ):
			np.random.seed(args.seed   )
			torch.manual_seed(args.seed   )
			if args.n_gpu > 0:
						torch.cuda.manual_seed_all(args.seed   )



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


@require_tf
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)