





"""simple docstring"""


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      lowercase_				(     unittest.TestCase							):

       @slow
       @unittest.skip("Model is not available."     )
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"nvidia/megatron-gpt2-345m/"
        if "MYDIR" in os.environ:
         snake_case__    							=				os.path.join(os.environ["MYDIR"]   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
        model.to(__SCREAMING_SNAKE_CASE     )
        model.half()

        snake_case__    							=				torch.tensor(
            [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]   ,					device=__SCREAMING_SNAKE_CASE   ,					dtype=torch.long   ,					)

        with torch.no_grad():
         snake_case__    							=				model(__SCREAMING_SNAKE_CASE     ).logits

        snake_case__    							=				torch.Size((1, 9, 5_0257)     )
        self.assertEqual(output.shape   ,					__SCREAMING_SNAKE_CASE     )

        snake_case__    							=				torch.tensor(
            [
                4.9414,
                -0.2920,
                -1.2148,
                -4.0273,
                -0.5161,
                -5.2109,
                -1.2412,
                -1.8301,
                -1.7734,
                -4.7148,
                -0.2317,
                -1.0811,
                -2.1777,
                0.4141,
                -3.7969,
                -4.0586,
                -2.5332,
                -3.3809,
                4.3867,
            ]   ,					device=__SCREAMING_SNAKE_CASE   ,					dtype=torch.half   ,					)

        for i in range(19     ):
         snake_case__							,   snake_case__    							=				8 * i // 17, 2792 * i  # along the diagonal
         snake_case__							,   snake_case__    							=				output[0, r, c], expected_diag[i]
         snake_case__    							=				F"row={r} col={c} computed={computed} expected={expected}"
         self.assertAlmostEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					delta=1e-4   ,					msg=__SCREAMING_SNAKE_CASE     )








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

