
"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def  lowercase						(	lowerCAmelCase__						):
			return ConvertCommand(
			    args.model_type	,args.tf_checkpoint	,args.pytorch_dump_output	,args.config	,args.finetuning_task_name						)


A_    							=					"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""




class  __lowerCamelCase  ( lowerCAmelCase			):




				@staticmethod
				def        UpperCAmelCase__						(				UpperCAmelCase   ):
							lowerCamelCase_					=  parser.add_parser(
							    '''convert'''							,						help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''							,						)
							train_parser.add_argument('''--model_type'''							,						type=UpperCAmelCase							,						required=UpperCAmelCase							,						help='''Model\'s type.'''   )
							train_parser.add_argument(
							    '''--tf_checkpoint'''							,						type=UpperCAmelCase							,						required=UpperCAmelCase							,						help='''TensorFlow checkpoint path or folder.'''   )
							train_parser.add_argument(
							    '''--pytorch_dump_output'''							,						type=UpperCAmelCase							,						required=UpperCAmelCase							,						help='''Path to the PyTorch saved model output.'''   )
							train_parser.add_argument('''--config'''							,						type=UpperCAmelCase							,						default=''''''							,						help='''Configuration file path or folder.'''   )
							train_parser.add_argument(
							    '''--finetuning_task_name'''							,						type=UpperCAmelCase							,						default=UpperCAmelCase							,						help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,						)
							train_parser.set_defaults(func=UpperCAmelCase   )




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						*UpperCAmelCase							,						):
							lowerCamelCase_					=  logging.get_logger('''transformers-cli/converting'''   )

							self._logger.info(f"Loading model {model_type}"   )
							lowerCamelCase_					=  model_type
							lowerCamelCase_					=  tf_checkpoint
							lowerCamelCase_					=  pytorch_dump_output
							lowerCamelCase_					=  config
							lowerCamelCase_					=  finetuning_task_name





				def        UpperCAmelCase__						(				self   ):
							if self._model_type == "albert":
										try:
													from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
													    convert_tf_checkpoint_to_pytorch,
													)
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							elif self._model_type == "bert":
										try:
													from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
													    convert_tf_checkpoint_to_pytorch,
													)
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							elif self._model_type == "funnel":
										try:
													from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
													    convert_tf_checkpoint_to_pytorch,
													)
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							elif self._model_type == "t5":
										try:
													from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							elif self._model_type == "gpt":
										from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
										    convert_openai_checkpoint_to_pytorch,
										)

										convert_openai_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							elif self._model_type == "transfo_xl":
										try:
													from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
													    convert_transfo_xl_checkpoint_to_pytorch,
													)
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										if "ckpt" in self._tf_checkpoint.lower():
													lowerCamelCase_					=  self._tf_checkpoint
													lowerCamelCase_					=  ''''''
										else:
													lowerCamelCase_					=  self._tf_checkpoint
													lowerCamelCase_					=  ''''''
										convert_transfo_xl_checkpoint_to_pytorch(
										    UpperCAmelCase							,						self._config							,						self._pytorch_dump_output							,						UpperCAmelCase   )
							elif self._model_type == "gpt2":
										try:
													from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
													    convert_gpta_checkpoint_to_pytorch,
													)
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							elif self._model_type == "xlnet":
										try:
													from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
													    convert_xlnet_checkpoint_to_pytorch,
													)
										except ImportError:
													raise ImportError(UpperCAmelCase   )

										convert_xlnet_checkpoint_to_pytorch(
										    self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output							,						self._finetuning_task_name   )
							elif self._model_type == "xlm":
										from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
										    convert_xlm_checkpoint_to_pytorch,
										)

										convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint							,						self._pytorch_dump_output   )
							elif self._model_type == "lxmert":
										from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
										    convert_lxmert_checkpoint_to_pytorch,
										)

										convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint							,						self._pytorch_dump_output   )
							elif self._model_type == "rembert":
										from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
										    convert_rembert_tf_checkpoint_to_pytorch,
										)

										convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint							,						self._config							,						self._pytorch_dump_output   )
							else:
										raise ValueError(
										    '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'''   )





"""simple docstring"""


def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  generate_pascal_triangle(lowerCAmelCase__						)
			for row_idx in range(lowerCAmelCase__						):
						# Print left spaces
						for _ in range(num_rows - row_idx - 1						):
									print(end=''' '''						)
						# Print row values
						for col_idx in range(row_idx + 1						):
									if col_idx != row_idx:
												print(triangle[row_idx][col_idx]	,end=''' '''						)
									else:
												print(triangle[row_idx][col_idx]	,end=''''''						)
						print()






def  lowercase						(	lowerCAmelCase__						):

			if not isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						raise TypeError('''The input value of \'num_rows\' should be \'int\''''						)

			if num_rows == 0:
						return []
			elif num_rows < 0:
						raise ValueError(
						    '''The input value of \'num_rows\' should be greater than or equal to 0'''						)

			lowerCamelCase_					=  []
			for current_row_idx in range(lowerCAmelCase__						):
						lowerCamelCase_					=  populate_current_row(lowerCAmelCase__	,lowerCAmelCase__						)
						triangle.append(lowerCAmelCase__						)
			return triangle






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  [-1] * (current_row_idx + 1)
			# first and last elements of current row are equal to 1
			lowerCamelCase_		, lowerCamelCase_					=  1, 1
			for current_col_idx in range(1	,lowerCAmelCase__						):
						calculate_current_element(
						    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
			return current_row






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,):
			lowerCamelCase_					=  triangle[current_row_idx - 1][current_col_idx - 1]
			lowerCamelCase_					=  triangle[current_row_idx - 1][current_col_idx]
			lowerCamelCase_					=  above_to_left_elt + above_to_right_elt






def  lowercase						(	lowerCAmelCase__						):

			if not isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						raise TypeError('''The input value of \'num_rows\' should be \'int\''''						)

			if num_rows == 0:
						return []
			elif num_rows < 0:
						raise ValueError(
						    '''The input value of \'num_rows\' should be greater than or equal to 0'''						)

			lowerCamelCase_					=  [[1]]

			for row_index in range(1	,lowerCAmelCase__						):
						lowerCamelCase_					=  [0] + result[-1] + [0]
						lowerCamelCase_					=  row_index + 1
						# Calculate the number of distinct elements in a row
						lowerCamelCase_					=  sum(divmod(lowerCAmelCase__	,2						)						)
						lowerCamelCase_					=  [
						    temp_row[i - 1] + temp_row[i] for i in range(1	,distinct_elements + 1						)
						]
						lowerCamelCase_					=  row_first_half[: (row_index + 1) // 2]
						row_second_half.reverse()
						lowerCamelCase_					=  row_first_half + row_second_half
						result.append(lowerCAmelCase__						)

			return result






def  lowercase						(	):
			from collections.abc import Callable
			from timeit import timeit

			def benchmark_a_function(lowerCAmelCase__	,lowerCAmelCase__						) -> None:
						lowerCamelCase_					=  f"{func.__name__}({value})"
						lowerCamelCase_					=  timeit(f"__main__.{call}"	,setup='''import __main__'''						)
						# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
						print(f"{call:38} -- {timing:.4f} seconds"						)

			for value in range(15						):  # (1, 7, 14):
						for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
									benchmark_a_function(lowerCAmelCase__	,lowerCAmelCase__						)
						print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()



