



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       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






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

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

       with torch.no_grad():
           UpperCAmelCase_     :       str  		=					model(lowerCamelCase_							).logits

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

       UpperCAmelCase_     :       Any  		=					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=lowerCamelCase_  ,dtype=torch.half  ,)

       for i in range(19							):
           UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					8 * i // 17, 2792 * i  # along the diagonal
           UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					output[0, r, c], expected_diag[i]
           UpperCAmelCase_     :       Dict  		=					F'''row={r} col={c} computed={computed} expected={expected}'''
           self.assertAlmostEqual(lowerCamelCase_  ,lowerCamelCase_  ,delta=1e-4  ,msg=lowerCamelCase_							)





# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List
def   UpperCAmelCase							(		A__				,  A__     )							->					Any:
	if name != "training_function" and name != "main":
		raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''     )
	_snake_case     , _snake_case					:							Union[str, Any]					     =   [], False
	for line in lines:
		if not found_start and f'''def {name}''' in line:
			_snake_case					:							Tuple					     =   True
			good_lines.append(A__     )
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(A__     )
def   UpperCAmelCase							(		A__     )							->					Dict:
	return [line for line in lines if not line.lstrip().startswith("""#"""     ) and line != "\n"]


def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__ = None     )							->					str:
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Tuple					     =   f.readlines()
	with open(os.path.abspath(os.path.join("""examples"""				,  """nlp_example.py"""     )     )				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	with open(A__				,  """r"""     ) as f:
		_snake_case					:							Optional[Any]					     =   f.readlines()
	if secondary_filename is not None:
		with open(A__				,  """r"""     ) as f:
			_snake_case					:							List[str]					     =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		_snake_case					:							Dict					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							int					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
		if secondary_filename is not None:
			_snake_case					:							Optional[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """main"""     )     )
	else:
		_snake_case					:							str					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							List[Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		_snake_case					:							Union[str, Any]					     =   clean_lines(get_function_contents_by_name(A__				,  """training_function"""     )     )
		if secondary_filename is not None:
			_snake_case					:							List[Any]					     =   clean_lines(
			    get_function_contents_by_name(A__				,  """training_function"""     )     )

	_snake_case					:							str					     =   """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

	# Specific code in our script that differs from the full version, aka what is new
	_snake_case					:							Dict					     =   []
	_snake_case					:							str					     =   []  # We keep track of the idxs just in case it's a repeated statement
	_snake_case					:							Optional[Any]					     =   iter(A__     )
	for i in range(len(A__     ) - 1     ):
		if i not in passed_idxs:
			_snake_case					:							Any					     =   next(A__     )
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(A__     )
					passed_idxs.append(A__     )
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					_snake_case					:							Optional[int]					     =   next(A__     )

    # Extract out just the new parts from the full_file_training_func
	_snake_case					:							Any					     =   []
	_snake_case					:							Tuple					     =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(A__     ):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(A__     )
					passed_idxs.append(A__     )

    # Finally, get the overall diff
	_snake_case					:							Optional[int]					     =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		_snake_case					:							Optional[int]					     =   [line for line in full_file_contents if line not in secondary_file_func]
		_snake_case					:							int					     =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example





