"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def        a_  (   _lowerCAmelCase						:				Namespace  ):





       '''simple docstring'''

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


_UpperCamelCase     :		List[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   UpperCAmelCase_			(					_a):


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


      def __init__(     self	,    a	,    a	,    a	,    a	,    a	,    *a	,    ) ->      Union[str, Any]:
             lowercase__    :	List[str]     					=			logging.get_logger('transformers-cli/converting'							)

             self._logger.info(f"""Loading model {model_type}"""							)
             lowercase__    :	int     					=			model_type
             lowercase__    :	Dict     					=			tf_checkpoint
             lowercase__    :	Union[str, Any]     					=			pytorch_dump_output
             lowercase__    :	int     					=			config
             lowercase__    :	str     					=			finetuning_task_name


      def 	_UpperCAmelCase (     self							) ->      Dict:
             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(a							)

                    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(a							)

                    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(a							)

                    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(a							)

                    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(a							)

                    if "ckpt" in self._tf_checkpoint.lower():
                           lowercase__    :	Optional[int]     					=			self._tf_checkpoint
                           lowercase__    :	int     					=			''
                    else:
                           lowercase__    :	Optional[int]     					=			self._tf_checkpoint
                           lowercase__    :	List[str]     					=			''
                    convert_transfo_xl_checkpoint_to_pytorch(
                        a	,    self._config	,    self._pytorch_dump_output	,    a							)
             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(a							)

                    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(a							)

                    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 gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

