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

	lowerCamelCase_    :		List[str]							=	True
except ImportError:
	lowerCamelCase_    :		Optional[int]							=	False

lowerCamelCase_    :		str							=	logging.get_logger(__name__)  # pylint: disable=invalid-name
def 							lowerCAmelCase(     __lowerCamelCase						):
					return AddNewModelCommand(args.testing				,    args.testing_file				,    path=args.path						)



class 						a__							(       __snake_case		):



			@staticmethod
			def __SCREAMING_SNAKE_CASE							(						UpperCAmelCase   )			->   Optional[Any]:
								__a		 =       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   )			->   List[Any]:
								__a		 =       testing
								__a		 =       testing_file
								__a		 =       path







			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								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
								__a		 =       [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:2_2]]
								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.'   )

								__a		 =       (
								    Path(UpperCAmelCase   ).parent.parent.parent.parent if self._path is None else Path(self._path   ).parent.parent
								)
								__a		 =       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:
																		__a		 =       json.load(UpperCAmelCase   )

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

								__a		 =       [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:2_2]][0]

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

								__a		 =       configuration['lowercase_modelname']
								__a		 =       configuration['generate_tensorflow_pytorch_and_flax']
								os.remove(f'''{directory}/configuration.json'''   )

								__a		 =       'PyTorch' in generate_tensorflow_pytorch_and_flax
								__a		 =       'TensorFlow' in generate_tensorflow_pytorch_and_flax
								__a		 =       'Flax' in generate_tensorflow_pytorch_and_flax

								__a		 =       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:
																		__a		 =       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
													__a ,				__a		 =       mkstemp()
													__a		 =       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:
																																	__a		 =       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:
																		__a		 =       []
																		__a		 =       False
																		__a		 =       False
																		for line in datafile:
																							if "# To replace in: " in line and "##" not in line:
																												__a		 =       line.split('"'   )[1]
																												__a		 =       skip_units(UpperCAmelCase   )
																							elif "# Below: " in line and "##" not in line:
																												__a		 =       line.split('"'   )[1]
																												__a		 =       skip_units(UpperCAmelCase   )
																							elif "# End." in line and "##" not in line:
																												if not skip_file and not skip_snippet:
																																	replace(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

																												__a		 =       []
																							elif "# Replace with" in line and "##" not in line:
																												__a		 =       []
																							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   )





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from transformers.modeling_outputs import BaseModelOutput

	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_    :		Dict							=	logging.get_logger(__name__)


@add_end_docstrings(__snake_case		)
class 						a__							(       __snake_case		):



			def __init__(						self				,						**UpperCAmelCase   )			->   List[str]:
								super().__init__(**UpperCAmelCase   )

								if self.framework == "tf":
													raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''   )

								requires_backends(self				,						'vision'   )
								self.check_model_type(UpperCAmelCase   )



			def __call__(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   Tuple:
								if "text_queries" in kwargs:
													__a		 =       kwargs.pop('text_queries'   )

								if isinstance(UpperCAmelCase				,						(str, Image.Image)   ):
													__a		 =       {'image': image, 'candidate_labels': candidate_labels}
								else:
													__a		 =       image
								__a		 =       super().__call__(UpperCAmelCase				,						**UpperCAmelCase   )
								return results



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   List[str]:
								__a		 =       {}
								if "threshold" in kwargs:
													__a		 =       kwargs['threshold']
								if "top_k" in kwargs:
													__a		 =       kwargs['top_k']
								return {}, {}, postprocess_params



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       load_image(inputs['image']   )
								__a		 =       inputs['candidate_labels']
								if isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       candidate_labels.split(','   )

								__a		 =       torch.tensor([[image.height, image.width]]				,						dtype=torch.intaa   )
								for i, candidate_label in enumerate(UpperCAmelCase   ):
													__a		 =       self.tokenizer(UpperCAmelCase				,						return_tensors=self.framework   )
													__a		 =       self.image_processor(UpperCAmelCase				,						return_tensors=self.framework   )
													yield {
													    "is_last": i == len(UpperCAmelCase   ) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								__a		 =       model_inputs.pop('target_size'   )
								__a		 =       model_inputs.pop('candidate_label'   )
								__a		 =       model_inputs.pop('is_last'   )

								__a		 =       self.model(**UpperCAmelCase   )

								__a		 =       {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
								return model_outputs



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=0.1				,						UpperCAmelCase=None   )			->   Tuple:
								__a		 =       []
								for model_output in model_outputs:
													__a		 =       model_output['candidate_label']
													__a		 =       BaseModelOutput(UpperCAmelCase   )
													__a		 =       self.image_processor.post_process_object_detection(
													    outputs=UpperCAmelCase				,						threshold=UpperCAmelCase				,						target_sizes=model_output['target_size']   )[0]

													for index in outputs["scores"].nonzero():
																		__a		 =       outputs['scores'][index].item()
																		__a		 =       self._get_bounding_box(outputs['boxes'][index][0]   )

																		__a		 =       {'score': score, 'label': label, 'box': box}
																		results.append(UpperCAmelCase   )

								__a		 =       sorted(UpperCAmelCase				,						key=lambda UpperCAmelCase   : x["score"]				,						reverse=UpperCAmelCase   )
								if top_k:
													__a		 =       results[:top_k]

								return results







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict[str, int]:
								if self.framework != "pt":
													raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
								__a ,				__a ,				__a ,				__a		 =       box.int().tolist()
								__a		 =       {
								    'xmin': xmin,
								    'ymin': ymin,
								    'xmax': xmax,
								    'ymax': ymax,
								}
								return bbox




