
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_UpperCAmelCase   :					List[str]    =			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					List[str]    =			["XLMProphetNetTokenizer"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Dict    =			[
					    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMProphetNetDecoder",
					    "XLMProphetNetEncoder",
					    "XLMProphetNetForCausalLM",
					    "XLMProphetNetForConditionalGeneration",
					    "XLMProphetNetModel",
					    "XLMProphetNetPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_prophetnet import (
										    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMProphetNetDecoder,
										    XLMProphetNetEncoder,
										    XLMProphetNetForCausalLM,
										    XLMProphetNetForConditionalGeneration,
										    XLMProphetNetModel,
										    XLMProphetNetPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase   :					Any    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

"""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        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

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

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

									for i in range(19  ):
														lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					8 * i // 17, 27_92 * i  # along the diagonal
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					output[0, r, c], expected_diag[i]
														lowerCamelCase__		:   Dict  				=					F"""row={r} col={c} computed={computed} expected={expected}"""
														self.assertAlmostEqual(__UpperCAmelCase      ,__UpperCAmelCase      ,delta=1E-4      ,msg=__UpperCAmelCase  )






