
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 					lowerCAmelCase__		(				self       :				Dict					):
							UpperCamelCase_:							Optional[int]		=  """nvidia/megatron-gpt2-345m/"""
							if "MYDIR" in os.environ:
											UpperCamelCase_:							Optional[Any]		=  os.path.join(os.environ["""MYDIR"""]  ,		snake_case_					)
							UpperCamelCase_:							List[str]		=  GPTaLMHeadModel.from_pretrained(snake_case_					)
							model.to(snake_case_					)
							model.half()

							UpperCamelCase_:							Union[str, Any]		=  torch.tensor(
							    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]  ,		device=snake_case_  ,		dtype=torch.long  ,		)

							with torch.no_grad():
											UpperCamelCase_:							List[Any]		=  model(snake_case_					).logits

							UpperCamelCase_:							Optional[Any]		=  torch.Size((1, 9, 5_0257)					)
							self.assertEqual(output.shape  ,		snake_case_					)

							UpperCamelCase_:							List[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(19					):
											UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  8 * i // 17, 2792 * i  # along the diagonal
											UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  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_					)








import torch

from transformers import AutoTokenizer

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				List[str]  ,		snake_case_       :				Dict					):
							UpperCamelCase_:							int		=  AutoTokenizer.from_pretrained(snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Any]					):

							if isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							Tuple		=  None
											for l in x:
															UpperCamelCase_:							Optional[Any]		=  self.tokenizer(
															    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)
															UpperCamelCase_:							str		=  torch.tensor([len(snake_case_					)]					)
															if d is not None:
																			for k in d.keys():
																							UpperCamelCase_:							List[Any]		=  torch.cat((d[k], t[k])  ,		0					)
															else:
																			UpperCamelCase_:							Optional[int]		=  t

											UpperCamelCase_:							Union[str, Any]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""					)					)
											UpperCamelCase_:							List[str]		=  torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""					)					)

							elif isinstance(snake_case_  ,		snake_case_					) and all(isinstance(_x  ,		snake_case_					) for _x in x					):
											UpperCamelCase_:							List[str]		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=384  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)

							else:
											raise Exception(
											    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
											    """ strings` for supports are supported."""					)

							return d




			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				List[str]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				List[Any]=0.70					):
							UpperCamelCase_:							int		=  []
							for idx in range(len(W_query["""input_ids"""]					)					):
											UpperCamelCase_:							List[str]		=  range(p_start.shape[1]					)

											UpperCamelCase_:							str		=  []
											for start_id in start_indexes:
															for end_id in end_indexes:
																			if start_id < end_id:
																							output.append(
																							    (
																							        start_id,
																							        end_id,
																							        p_start[idx][start_id].item(),
																							        p_end[idx][end_id].item(),
																							    )					)

											output.sort(key=lambda snake_case_					: (tup[2] * tup[3])  ,		reverse=snake_case_					)
											UpperCamelCase_:							Optional[int]		=  []
											for k in range(len(snake_case_					)					):
															if output[k][2] * output[k][3] >= thresh:
																			UpperCamelCase_,      UpperCamelCase_:							Tuple		=  output[k][0], output[k][1]
																			UpperCamelCase_:							Any		=  self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]					)
																			temp.append((decoded, output[k][2] * output[k][3])					)

											final_outputs.append(snake_case_					)

							return final_outputs






