


import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):


     @slow
     @unittest.skip("""Model is not available."""						)
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Optional[Any]       =    """nvidia/megatron-gpt2-345m/"""
       if "MYDIR" in os.environ:
         _UpperCAmelCase		:       List[str]       =    os.path.join(os.environ["""MYDIR"""]       ,				A_						)
       _UpperCAmelCase		:       str       =    GPTaLMHeadModel.from_pretrained(A_						)
       model.to(A_						)
       model.half()

       _UpperCAmelCase		:       Dict       =    torch.tensor(
           [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]       ,				device=A_       ,				dtype=torch.long       ,				)

       with torch.no_grad():
         _UpperCAmelCase		:       Optional[int]       =    model(A_						).logits

       _UpperCAmelCase		:       List[Any]       =    torch.Size((1, 9, 5_02_57)						)
       self.assertEqual(output.shape       ,				A_						)

       _UpperCAmelCase		:       Optional[Any]       =    torch.tensor(
           [
               4.9_4_1_4,
               -0.2_9_2_0,
               -1.2_1_4_8,
               -4.0_2_7_3,
               -0.5_1_6_1,
               -5.2_1_0_9,
               -1.2_4_1_2,
               -1.8_3_0_1,
               -1.7_7_3_4,
               -4.7_1_4_8,
               -0.2_3_1_7,
               -1.0_8_1_1,
               -2.1_7_7_7,
               0.4_1_4_1,
               -3.7_9_6_9,
               -4.0_5_8_6,
               -2.5_3_3_2,
               -3.3_8_0_9,
               4.3_8_6_7,
           ]       ,				device=A_       ,				dtype=torch.half       ,				)

       for i in range(19						):
         _UpperCAmelCase,_UpperCAmelCase		:       int       =    8 * i // 17, 27_92 * i  # along the diagonal
         _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    output[0, r, c], expected_diag[i]
         _UpperCAmelCase		:       Tuple       =    F'''row={r} col={c} computed={computed} expected={expected}'''
         self.assertAlmostEqual(A_       ,				A_       ,				delta=1e-4       ,				msg=A_						)









from typing import Any


class 		_SCREAMING_SNAKE_CASE    :


     def __init__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    []
       _UpperCAmelCase		:       Dict       =    0


     def __str__(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
       return printed


     def 					__snake_case(     self       ,				A_						):
       self.stack.append(A_						)
       _UpperCAmelCase		:       Optional[Any]       =    self.length + 1


     def 					__snake_case(     self						):
       self.rotate(1						)
       _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
       _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
       self.rotate(self.length - 1						)
       _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
       return dequeued


     def 					__snake_case(     self       ,				A_						):
       for _ in range(A_						):
         _UpperCAmelCase		:       Dict       =    self.stack[0]
         _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
         self.put(A_						)
         _UpperCAmelCase		:       Dict       =    self.length - 1


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Tuple       =    self.get()
       self.put(A_						)
       self.rotate(self.length - 1						)
       return front


     def 					__snake_case(     self						):
       return self.length





