
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase = False							):





    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(A_							)
    np.random.seed(A_							)
    torch.manual_seed(A_							)
    if is_xpu_available():
        torch.xpu.manual_seed_all(A_							)
    elif is_npu_available():
        torch.npu.manual_seed_all(A_							)
    else:
        torch.cuda.manual_seed_all(A_							)
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(A_							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase = None      ,						__lowerCAmelCase = None							):





    if rng_type == RNGType.TORCH:
        snake_case__  =			torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        snake_case__  =			torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        snake_case__  =			torch.tensor(xm.get_rng_state()							)
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        snake_case__  =			torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        snake_case__  =			torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        snake_case__  =			generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    snake_case__  =			AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        snake_case__  =			rng_state.to(xm.xla_device()							)
        xm.collective_broadcast([rng_state]							)
        xm.mark_step()
        snake_case__  =			rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        snake_case__  =			rng_state.to(state.device							)
        torch.distributed.broadcast(A_      ,						0							)
        snake_case__  =			rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(A_      ,						0							)

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(A_							)
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(A_							)
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(A_							)
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(A_							)
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()							)
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(A_							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase = None							):





    for rng_type in rng_types:
        synchronize_rng_state(RNGType(A_							)      ,						generator=A_							)





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




@require_tokenizers
class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      _A  :						Optional[Any]      		=       EsmTokenizer
      def A_     (     self	):
          super().setUp()
          snake_case__  =			tempfile.mkdtemp()
          # fmt: off
          snake_case__  =			["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
          # fmt: on
          snake_case__  =			os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["vocab_file"]	)
          with open(self.vocab_file						,		"w"						,		encoding="utf-8"	) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)
      def A_     (     self						,		**lowerCamelCase	):
          return [self.get_tokenizer(**lowerCamelCase	)]
      def A_     (     self						,		**lowerCamelCase	):
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,		**lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			tokenizer.tokenize("LAGVS"	)
          self.assertListEqual(lowerCamelCase						,		["L", "A", "G", "V", "S"]	)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,		[4, 5, 6, 7, 8]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			"LAGVS"
          self.assertListEqual(tokenizer.encode(lowerCamelCase	)						,		[0, 4, 5, 6, 7, 8, 2]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			["LAGVS", "WCB"]
          snake_case__  =			tokenizer(lowerCamelCase						,		padding=lowerCamelCase	)["input_ids"]

          self.assertListEqual(lowerCamelCase						,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]	)
      def A_     (     self	):
          snake_case__  =			self.tokenizer_class(self.vocab_file	)

          snake_case__  =			["LAGVS", "WCB"]
          snake_case__  =			tokenizer(lowerCamelCase						,		padding=lowerCamelCase	)["input_ids"]

          self.assertListEqual(lowerCamelCase						,		[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]	)



      def A_     (     self	):
          snake_case__  =			self.get_tokenizers(fast=lowerCamelCase	)
          for tokenizer in tokenizers:
              with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
                  snake_case__  =			"<unk>"
                  snake_case__  =			"<mask>"

                  snake_case__  =			tokenizer.tokenize(lowerCamelCase	)
                  snake_case__  =			tokenizer.tokenize(lowerCamelCase	)

                  self.assertEqual(len(lowerCamelCase	)						,		1	)
                  self.assertEqual(len(lowerCamelCase	)						,		1	)
                  self.assertEqual(token_a[0]						,		lowerCamelCase	)
                  self.assertEqual(token_a[0]						,		lowerCamelCase	)



