


"""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   __A  (unittest.TestCase):





							'''simple docstring'''

							@slow
							@unittest.skip("""Model is not available."""							)
							def 		lowerCAmelCase							(					self				:   Optional[int]							)       ->List[str]:



														"""simple docstring"""



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

														snake_case_     							=		torch.tensor(
														    [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]		,							device=UpperCAmelCase_		,							dtype=torch.long		,							)

														with torch.no_grad():
																					snake_case_     							=		model(UpperCAmelCase_							).logits

														snake_case_     							=		torch.Size((1, 9, 50_257)							)
														self.assertEqual(output.shape		,							UpperCAmelCase_							)

														snake_case_     							=		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							):
																					snake_case_							,    snake_case_     							=		8 * i // 17, 2_792 * 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(UpperCAmelCase_		,							UpperCAmelCase_		,							delta=1E-4		,							msg=UpperCAmelCase_							)







"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



