






"""simple docstring"""



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Namespace    ):





  '''simple docstring'''





  return ConvertCommand(
      args.model_type		,					args.tf_checkpoint		,					args.pytorch_dump_output		,					args.config		,					args.finetuning_task_name    )


_UpperCamelCase		:  Union[str, Any]						=		'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'



class        a     (   a_	):







    @staticmethod
    def 			UpperCamelCase_      (			_lowerCamelCase       ):
      lowercase							    =       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=_lowerCamelCase							,		required=_lowerCamelCase							,		help='Model\'s type.'       )
      train_parser.add_argument(
          '--tf_checkpoint'							,		type=_lowerCamelCase							,		required=_lowerCamelCase							,		help='TensorFlow checkpoint path or folder.'       )
      train_parser.add_argument(
          '--pytorch_dump_output'							,		type=_lowerCamelCase							,		required=_lowerCamelCase							,		help='Path to the PyTorch saved model output.'       )
      train_parser.add_argument('--config'							,		type=_lowerCamelCase							,		default=''							,		help='Configuration file path or folder.'       )
      train_parser.add_argument(
          '--finetuning_task_name'							,		type=_lowerCamelCase							,		default=_lowerCamelCase							,		help='Optional fine-tuning task name if the TF model was a finetuned model.'							,		)
      train_parser.set_defaults(func=_lowerCamelCase       )







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

      self._logger.info(F'Loading model {model_type}'       )
      lowercase							    =       model_type
      lowercase							    =       tf_checkpoint
      lowercase							    =       pytorch_dump_output
      lowercase							    =       config
      lowercase							    =       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(_lowerCamelCase       )

        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(_lowerCamelCase       )

        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(_lowerCamelCase       )

        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(_lowerCamelCase       )

        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(_lowerCamelCase       )

        if "ckpt" in self._tf_checkpoint.lower():
          lowercase							    =       self._tf_checkpoint
          lowercase							    =       ''
        else:
          lowercase							    =       self._tf_checkpoint
          lowercase							    =       ''
        convert_transfo_xl_checkpoint_to_pytorch(
            _lowerCamelCase							,		self._config							,		self._pytorch_dump_output							,		_lowerCamelCase       )
      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(_lowerCamelCase       )

        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(_lowerCamelCase       )

        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"""



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor

class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							    =       size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       min_resolution
      lowercase							    =       max_resolution
      lowercase							    =       do_resize
      lowercase							    =       size
      lowercase							    =       do_normalize
      lowercase							    =       image_mean
      lowercase							    =       image_std







    def 			UpperCamelCase_      (			self       ):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[int]	     =DPTImageProcessor if is_vision_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       DPTImageProcessingTester(self       )







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}       )

      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2       )
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image       )

      # Test not batched input
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray       )

      # Test not batched input
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor       )

      # Test not batched input
      lowercase							    =       image_processing(image_inputs[0]							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		return_tensors='pt'       ).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


