
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE     :   Tuple							 =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :   str							 =      {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"convnext"





	def __init__(      self					,							UpperCamelCase__=3					,							UpperCamelCase__=4					,							UpperCamelCase__=4					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__="gelu"					,							UpperCamelCase__=0.0_2					,							UpperCamelCase__=1e-12					,							UpperCamelCase__=1e-6					,							UpperCamelCase__=0.0					,							UpperCamelCase__=224					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							**UpperCamelCase__					,							):
					super().__init__(**UpperCamelCase__		)

					A__	:						List[Any]								=				num_channels
					A__	:						int								=				patch_size
					A__	:						Optional[int]								=				num_stages
					A__	:						Optional[Any]								=				[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
					A__	:						str								=				[3, 3, 9, 3] if depths is None else depths
					A__	:						Optional[int]								=				hidden_act
					A__	:						Tuple								=				initializer_range
					A__	:						Union[str, Any]								=				layer_norm_eps
					A__	:						str								=				layer_scale_init_value
					A__	:						Dict								=				drop_path_rate
					A__	:						str								=				image_size
					A__	:						List[Any]								=				['''stem'''] + [F"stage{idx}" for idx in range(1					,							len(self.depths		) + 1		)]
					A__							,				A__	:						Optional[int]								=				get_aligned_output_features_output_indices(
					    out_features=UpperCamelCase__					,							out_indices=UpperCamelCase__					,							stage_names=self.stage_names		)







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					version.parse("1.11" )





	@property
	def 	__snake_case					(      self		):
					return OrderedDict(
					    [
					        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
					    ]		)





	@property
	def 	__snake_case					(      self		):
					return 1e-5

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 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





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

					A__	:						Dict								=				torch.tensor(
					    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]					,							device=UpperCamelCase__					,							dtype=torch.long					,							)

					with torch.no_grad():
									A__	:						str								=				model(UpperCamelCase__		).logits

					A__	:						int								=				torch.Size((1, 9, 5_0257)		)
					self.assertEqual(output.shape					,							UpperCamelCase__		)

					A__	:						Dict								=				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=UpperCamelCase__					,							dtype=torch.half					,							)

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