

'''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   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

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


							'''simple docstring'''

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

							UpperCAmelCase		:							Optional[int]        =       torch.tensor(
							    [[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   ,      device=snake_case   ,      dtype=torch.long   ,      )

							with torch.no_grad():
												UpperCAmelCase		:							Optional[Any]        =       model(snake_case      ).logits

							UpperCAmelCase		:							Any        =       torch.Size((1, 9, 5_0_2_5_7)      )
							self.assertEqual(output.shape   ,      snake_case      )

							UpperCAmelCase		:							Union[str, Any]        =       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=snake_case   ,      dtype=torch.half   ,      )

							for i in range(1_9      ):
												UpperCAmelCase ,			UpperCAmelCase		:							int        =       8 * i // 1_7, 2_7_9_2 * i  # along the diagonal
												UpperCAmelCase ,			UpperCAmelCase		:							str        =       output[0, r, c], expected_diag[i]
												UpperCAmelCase		:							Any        =       f"row={r} col={c} computed={computed} expected={expected}"
												self.assertAlmostEqual(snake_case   ,      snake_case   ,      delta=1e-4   ,      msg=snake_case      )



'''simple docstring'''

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


a       :      List[Any]  	=			logging.get_logger(__name__)

a       :      Dict  	=			{
    "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
}







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							"mobilevitv2"







		def __init__(			self   ,      snake_case=3   ,      snake_case=2_5_6   ,      snake_case=2   ,      snake_case=2.0   ,      snake_case="swish"   ,      snake_case=3   ,      snake_case=3_2   ,      snake_case=0.1   ,      snake_case=0.02   ,      snake_case=1e-5   ,      snake_case=5_1_2   ,      snake_case=[6, 1_2, 1_8]   ,      snake_case=0.1   ,      snake_case=2_5_5   ,      snake_case=[2, 4, 3]   ,      snake_case=[1_2_8, 1_9_2, 2_5_6]   ,      snake_case=1.0   ,      snake_case=2   ,      snake_case=0.0   ,      snake_case=0.0   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(**snake_case      )

							UpperCAmelCase		:							Tuple        =       num_channels
							UpperCAmelCase		:							Optional[int]        =       image_size
							UpperCAmelCase		:							Optional[int]        =       patch_size
							UpperCAmelCase		:							Optional[int]        =       expand_ratio
							UpperCAmelCase		:							List[Any]        =       hidden_act
							UpperCAmelCase		:							Optional[Any]        =       conv_kernel_size
							UpperCAmelCase		:							str        =       output_stride
							UpperCAmelCase		:							Optional[Any]        =       initializer_range
							UpperCAmelCase		:							Optional[Any]        =       layer_norm_eps
							UpperCAmelCase		:							List[str]        =       n_attn_blocks
							UpperCAmelCase		:							Any        =       base_attn_unit_dims
							UpperCAmelCase		:							str        =       width_multiplier
							UpperCAmelCase		:							Optional[int]        =       ffn_multiplier
							UpperCAmelCase		:							List[Any]        =       ffn_dropout
							UpperCAmelCase		:							List[str]        =       attn_dropout
							UpperCAmelCase		:							Optional[Any]        =       classifier_dropout_prob

							# decode head attributes for semantic segmentation
							UpperCAmelCase		:							Optional[Any]        =       aspp_out_channels
							UpperCAmelCase		:							Dict        =       atrous_rates
							UpperCAmelCase		:							int        =       aspp_dropout_prob
							UpperCAmelCase		:							List[str]        =       semantic_loss_ignore_index







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							version.parse("1.11"       )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]      )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							if self.task == "image-classification":
												return OrderedDict([("logits", {0: "batch"})]      )
							else:
												return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return 1e-4
