



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 							_a							(      unittest.TestCase				):

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

						UpperCAmelCase_:		int       			=    torch.tensor(
						    [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]],      device=SCREAMING_SNAKE_CASE_,      dtype=torch.long,      )

						with torch.no_grad():
									UpperCAmelCase_:		Union[str, Any]       			=    model(SCREAMING_SNAKE_CASE_				).logits

						UpperCAmelCase_:		Union[str, Any]       			=    torch.Size((1, 9, 50257)				)
						self.assertEqual(output.shape,      SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		str       			=    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=SCREAMING_SNAKE_CASE_,      dtype=torch.half,      )

						for i in range(19				):
									UpperCAmelCase_			,       UpperCAmelCase_:		Tuple       			=    8 * i // 17, 2792 * i  # along the diagonal
									UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    output[0, r, c], expected_diag[i]
									UpperCAmelCase_:		Any       			=    f'row={r} col={c} computed={computed} expected={expected}'
									self.assertAlmostEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      delta=1E-4,      msg=SCREAMING_SNAKE_CASE_				)











from typing import TYPE_CHECKING

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


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






