






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
							from cookiecutter.main import cookiecutter

							__snake_case    :    int         =     True
except ImportError:
							__snake_case    :    Tuple         =     False

__snake_case    :    int         =     logging.get_logger(__name__)  # pylint: disable=invalid-name







def 	_UpperCamelCase   (    UpperCamelCase_       :					Namespace		)  ->   str:

						"""simple docstring"""



						return AddNewModelCommand(args.testing    ,       args.testing_file    ,       path=args.path		)


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						@staticmethod
						def       UpperCamelCase__      (	_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						parser.add_parser('add-new-model'   )
												add_new_model_parser.add_argument('--testing'     ,						action='store_true'     ,						help='If in testing mode.'   )
												add_new_model_parser.add_argument('--testing_file'     ,						type=_UpperCamelCase     ,						help='Configuration file on which to run.'   )
												add_new_model_parser.add_argument(
												    '--path'     ,						type=_UpperCamelCase     ,						help='Path to cookiecutter. Should only be used for testing purposes.'   )
												add_new_model_parser.set_defaults(func=_UpperCamelCase   )






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						*_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						testing
												lowerCAmelCase__    						=						testing_file
												lowerCAmelCase__    						=						path







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												warnings.warn(
												    'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
												    'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
												    'checks, you should use `transformers-cli add-new-model-like` instead.'   )
												if not _has_cookiecutter:
																		raise ImportError(
																		    'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
																		    'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'   )
												# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
												lowerCAmelCase__    						=						[directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
												if len(_UpperCamelCase   ) > 0:
																		raise ValueError(
																		    'Several directories starting with `cookiecutter-template-` in current working directory. '
																		    'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
																		    'change your working directory.'   )

												lowerCAmelCase__    						=						(
												    Path(_UpperCamelCase   ).parent.parent.parent.parent if self._path is None else Path(self._path   ).parent.parent
												)
												lowerCAmelCase__    						=						path_to_transformer_root / 'templates' / 'adding_a_new_model'

												# Execute cookiecutter
												if not self._testing:
																		cookiecutter(str(_UpperCamelCase   )   )
												else:
																		with open(self._testing_file     ,						'r'   ) as configuration_file:
																								lowerCAmelCase__    						=						json.load(_UpperCamelCase   )

																		cookiecutter(
																		    str(path_to_cookiecutter if self._path is None else self._path   )     ,						no_input=_UpperCamelCase     ,						extra_context=_UpperCamelCase     ,						)

												lowerCAmelCase__    						=						[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

												# Retrieve configuration
												with open(directory + '/configuration.json'     ,						'r'   ) as configuration_file:
																		lowerCAmelCase__    						=						json.load(_UpperCamelCase   )

												lowerCAmelCase__    						=						configuration['lowercase_modelname']
												lowerCAmelCase__    						=						configuration['generate_tensorflow_pytorch_and_flax']
												os.remove(F"{directory}/configuration.json"   )

												lowerCAmelCase__    						=						'PyTorch' in generate_tensorflow_pytorch_and_flax
												lowerCAmelCase__    						=						'TensorFlow' in generate_tensorflow_pytorch_and_flax
												lowerCAmelCase__    						=						'Flax' in generate_tensorflow_pytorch_and_flax

												lowerCAmelCase__    						=						F"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"
												os.makedirs(_UpperCamelCase     ,						exist_ok=_UpperCamelCase   )
												os.makedirs(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}"     ,						exist_ok=_UpperCamelCase   )

												# Tests require submodules as they have parent imports
												with open(F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"     ,						'w'   ):
																		pass

												shutil.move(
												    F"{directory}/__init__.py"     ,						F"{model_dir}/__init__.py"     ,						)
												shutil.move(
												    F"{directory}/configuration_{lowercase_model_name}.py"     ,						F"{model_dir}/configuration_{lowercase_model_name}.py"     ,						)

												def remove_copy_lines(_UpperCamelCase   ):
																		with open(_UpperCamelCase     ,						'r'   ) as f:
																								lowerCAmelCase__    						=						f.readlines()
																		with open(_UpperCamelCase     ,						'w'   ) as f:
																								for line in lines:
																														if "# Copied from transformers." not in line:
																																				f.write(_UpperCamelCase   )

												if output_pytorch:
																		if not self._testing:
																								remove_copy_lines(F"{directory}/modeling_{lowercase_model_name}.py"   )

																		shutil.move(
																		    F"{directory}/modeling_{lowercase_model_name}.py"     ,						F"{model_dir}/modeling_{lowercase_model_name}.py"     ,						)

																		shutil.move(
																		    F"{directory}/test_modeling_{lowercase_model_name}.py"     ,						F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"     ,						)
												else:
																		os.remove(F"{directory}/modeling_{lowercase_model_name}.py"   )
																		os.remove(F"{directory}/test_modeling_{lowercase_model_name}.py"   )

												if output_tensorflow:
																		if not self._testing:
																								remove_copy_lines(F"{directory}/modeling_tf_{lowercase_model_name}.py"   )

																		shutil.move(
																		    F"{directory}/modeling_tf_{lowercase_model_name}.py"     ,						F"{model_dir}/modeling_tf_{lowercase_model_name}.py"     ,						)

																		shutil.move(
																		    F"{directory}/test_modeling_tf_{lowercase_model_name}.py"     ,						F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"     ,						)
												else:
																		os.remove(F"{directory}/modeling_tf_{lowercase_model_name}.py"   )
																		os.remove(F"{directory}/test_modeling_tf_{lowercase_model_name}.py"   )

												if output_flax:
																		if not self._testing:
																								remove_copy_lines(F"{directory}/modeling_flax_{lowercase_model_name}.py"   )

																		shutil.move(
																		    F"{directory}/modeling_flax_{lowercase_model_name}.py"     ,						F"{model_dir}/modeling_flax_{lowercase_model_name}.py"     ,						)

																		shutil.move(
																		    F"{directory}/test_modeling_flax_{lowercase_model_name}.py"     ,						F"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"     ,						)
												else:
																		os.remove(F"{directory}/modeling_flax_{lowercase_model_name}.py"   )
																		os.remove(F"{directory}/test_modeling_flax_{lowercase_model_name}.py"   )

												shutil.move(
												    F"{directory}/{lowercase_model_name}.md"     ,						F"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"     ,						)

												shutil.move(
												    F"{directory}/tokenization_{lowercase_model_name}.py"     ,						F"{model_dir}/tokenization_{lowercase_model_name}.py"     ,						)

												shutil.move(
												    F"{directory}/tokenization_fast_{lowercase_model_name}.py"     ,						F"{model_dir}/tokenization_{lowercase_model_name}_fast.py"     ,						)

												from os import fdopen, remove
												from shutil import copymode, move
												from tempfile import mkstemp

												def replace(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):
																		# Create temp file
																		lowerCAmelCase__						,       lowerCAmelCase__    						=						mkstemp()
																		lowerCAmelCase__    						=						False
																		with fdopen(_UpperCamelCase     ,						'w'   ) as new_file:
																								with open(_UpperCamelCase   ) as old_file:
																														for line in old_file:
																																				new_file.write(_UpperCamelCase   )
																																				if line_to_copy_below in line:
																																										lowerCAmelCase__    						=						True
																																										for line_to_copy in lines_to_copy:
																																																new_file.write(_UpperCamelCase   )

																		if not line_found:
																								raise ValueError(F"Line {line_to_copy_below} was not found in file."   )

																		# Copy the file permissions from the old file to the new file
																		copymode(_UpperCamelCase     ,						_UpperCamelCase   )
																		# Remove original file
																		remove(_UpperCamelCase   )
																		# Move new file
																		move(_UpperCamelCase     ,						_UpperCamelCase   )

												def skip_units(_UpperCamelCase   ):
																		return (
																		    ("generating PyTorch" in line and not output_pytorch)
																		    or ("generating TensorFlow" in line and not output_tensorflow)
																		    or ("generating Flax" in line and not output_flax)
																		)

												def replace_in_files(_UpperCamelCase   ):
																		with open(_UpperCamelCase   ) as datafile:
																								lowerCAmelCase__    						=						[]
																								lowerCAmelCase__    						=						False
																								lowerCAmelCase__    						=						False
																								for line in datafile:
																														if "# To replace in: " in line and "##" not in line:
																																				lowerCAmelCase__    						=						line.split('"'   )[1]
																																				lowerCAmelCase__    						=						skip_units(_UpperCamelCase   )
																														elif "# Below: " in line and "##" not in line:
																																				lowerCAmelCase__    						=						line.split('"'   )[1]
																																				lowerCAmelCase__    						=						skip_units(_UpperCamelCase   )
																														elif "# End." in line and "##" not in line:
																																				if not skip_file and not skip_snippet:
																																										replace(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )

																																				lowerCAmelCase__    						=						[]
																														elif "# Replace with" in line and "##" not in line:
																																				lowerCAmelCase__    						=						[]
																														elif "##" not in line:
																																				lines_to_copy.append(_UpperCamelCase   )

																		remove(_UpperCamelCase   )

												replace_in_files(F"{directory}/to_replace_{lowercase_model_name}.py"   )
												os.rmdir(_UpperCamelCase   )













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





