import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
 UpperCAmelCase     						=			False


if is_vision_available():
 from PIL import Image
else:




 class 				__lowerCAmelCase					:



  '''simple docstring'''

  @staticmethod
  def    __snake_case   (     *lowerCamelCase    :						Union[str, Any]    ,     **lowerCamelCase    :						int					)	->							int:
         pass




@is_pipeline_test
@require_vision
class 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		List[Any]			      =	MODEL_FOR_VISION_2_SEQ_MAPPING
 _lowerCamelCase  :		List[str]			      =	TF_MODEL_FOR_VISION_2_SEQ_MAPPING

 def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						Optional[int]    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						Optional[Any]					)	->							Any:
        _UpperCamelCase       :    List[Any]					  =					pipeline('image-to-text'    ,     model=lowerCamelCase    ,     tokenizer=lowerCamelCase    ,     image_processor=lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					[
            Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					),
            './tests/fixtures/tests_samples/COCO/000000039769.png',
        ]
        return pipe, examples

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						Union[str, Any]    ,     lowerCamelCase    :						Optional[Any]					)	->							Optional[Any]:
        _UpperCamelCase       :    List[str]					  =					pipe(lowerCamelCase					)
        self.assertEqual(
            lowerCamelCase    ,     [
                [{'generated_text': ANY(lowerCamelCase					)}],
                [{'generated_text': ANY(lowerCamelCase					)}],
            ]    ,     )

 @require_tf
 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        _UpperCamelCase       :    Optional[int]					  =					pipeline('image-to-text'    ,     model='hf-internal-testing/tiny-random-vit-gpt2'    ,     framework='tf'					)
        _UpperCamelCase       :    Tuple					  =					'./tests/fixtures/tests_samples/COCO/000000039769.png'

        _UpperCamelCase       :    Optional[Any]					  =					pipe(lowerCamelCase					)
        self.assertEqual(
            lowerCamelCase    ,     [
                {
                    'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                },
            ]    ,     )

        _UpperCamelCase       :    Optional[Any]					  =					pipe([image, image]					)
        self.assertEqual(
            lowerCamelCase    ,     [
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
            ]    ,     )

        _UpperCamelCase       :    Any					  =					pipe(lowerCamelCase    ,     max_new_tokens=1					)
        self.assertEqual(
            lowerCamelCase    ,     [{'generated_text': 'growth'}]    ,     )

 @require_torch
 def    __snake_case   (     self    :						List[Any]					)	->							Optional[int]:
        _UpperCamelCase       :    str					  =					pipeline('image-to-text'    ,     model='hf-internal-testing/tiny-random-vit-gpt2'					)
        _UpperCamelCase       :    Optional[Any]					  =					'./tests/fixtures/tests_samples/COCO/000000039769.png'

        _UpperCamelCase       :    str					  =					pipe(lowerCamelCase					)
        self.assertEqual(
            lowerCamelCase    ,     [
                {
                    'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                },
            ]    ,     )

        _UpperCamelCase       :    Dict					  =					pipe([image, image]					)
        self.assertEqual(
            lowerCamelCase    ,     [
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
            ]    ,     )

 @require_torch
 def    __snake_case   (     self    :						Optional[Any]					)	->							Tuple:
        _UpperCamelCase       :    Optional[Any]					  =					pipeline('image-to-text'    ,     model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'					)
        _UpperCamelCase       :    Union[str, Any]					  =					'./tests/fixtures/tests_samples/COCO/000000039769.png'
        _UpperCamelCase       :    Optional[int]					  =					'a photo of'

        _UpperCamelCase       :    Union[str, Any]					  =					pipe(lowerCamelCase    ,     prompt=lowerCamelCase					)
        self.assertTrue(outputs[0]['generated_text'].startswith(lowerCamelCase					)					)

 @slow
 @require_torch
 def    __snake_case   (     self    :						List[Any]					)	->							Any:
        _UpperCamelCase       :    Any					  =					pipeline('image-to-text'    ,     model='ydshieh/vit-gpt2-coco-en'					)
        _UpperCamelCase       :    Dict					  =					'./tests/fixtures/tests_samples/COCO/000000039769.png'

        _UpperCamelCase       :    Optional[Any]					  =					pipe(lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]					)

        _UpperCamelCase       :    str					  =					pipe([image, image]					)
        self.assertEqual(
            lowerCamelCase    ,     [
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
            ]    ,     )

 @slow
 @require_torch
 def    __snake_case   (     self    :						Dict					)	->							List[Any]:
        _UpperCamelCase       :    Dict					  =					pipeline('image-to-text'    ,     model='Salesforce/blip-image-captioning-base'					)
        _UpperCamelCase       :    Tuple					  =					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
        _UpperCamelCase       :    str					  =					Image.open(requests.get(lowerCamelCase    ,     stream=lowerCamelCase					).raw					)

        _UpperCamelCase       :    Union[str, Any]					  =					pipe(lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]					)

 @slow
 @require_torch
 def    __snake_case   (     self    :						List[str]					)	->							int:
        _UpperCamelCase       :    Any					  =					pipeline('image-to-text'    ,     model='microsoft/git-base-coco'					)
        _UpperCamelCase       :    Dict					  =					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
        _UpperCamelCase       :    List[Any]					  =					Image.open(requests.get(lowerCamelCase    ,     stream=lowerCamelCase					).raw					)

        _UpperCamelCase       :    Optional[int]					  =					pipe(lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'a cartoon of a purple character.'}]					)

 @slow
 @require_torch
 def    __snake_case   (     self    :						Any					)	->							Union[str, Any]:
        _UpperCamelCase       :    List[Any]					  =					pipeline('image-to-text'    ,     model='Salesforce/blip-image-captioning-base'					)
        _UpperCamelCase       :    Tuple					  =					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
        _UpperCamelCase       :    Any					  =					Image.open(requests.get(lowerCamelCase    ,     stream=lowerCamelCase					).raw					)

        _UpperCamelCase       :    Union[str, Any]					  =					'a photography of'

        _UpperCamelCase       :    Any					  =					pipe(lowerCamelCase    ,     prompt=lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'a photography of a volcano'}]					)

        with self.assertRaises(lowerCamelCase					):
               _UpperCamelCase       :    List[str]					  =					pipe([image, image]    ,     prompt=[prompt, prompt]					)

 @slow
 @require_torch
 def    __snake_case   (     self    :						List[str]					)	->							Dict:
        _UpperCamelCase       :    List[Any]					  =					pipeline('image-to-text'    ,     model='microsoft/git-base-coco'					)
        _UpperCamelCase       :    Dict					  =					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
        _UpperCamelCase       :    List[Any]					  =					Image.open(requests.get(lowerCamelCase    ,     stream=lowerCamelCase					).raw					)

        _UpperCamelCase       :    Any					  =					'a photo of a'

        _UpperCamelCase       :    List[Any]					  =					pipe(lowerCamelCase    ,     prompt=lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]					)

        with self.assertRaises(lowerCamelCase					):
               _UpperCamelCase       :    Any					  =					pipe([image, image]    ,     prompt=[prompt, prompt]					)

 @unittest.skipIf(
     not is_torch_greater_or_equal_than_1_11    ,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'					)
 @slow
 @require_torch
 def    __snake_case   (     self    :						List[Any]					)	->							List[Any]:
        _UpperCamelCase       :    str					  =					pipeline('image-to-text'    ,     model='google/pix2struct-ai2d-base'					)
        _UpperCamelCase       :    Optional[int]					  =					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
        _UpperCamelCase       :    Any					  =					Image.open(requests.get(lowerCamelCase    ,     stream=lowerCamelCase					).raw					)

        _UpperCamelCase       :    Any					  =					'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

        _UpperCamelCase       :    Tuple					  =					pipe(lowerCamelCase    ,     prompt=lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'ash cloud'}]					)

        with self.assertRaises(lowerCamelCase					):
               _UpperCamelCase       :    List[str]					  =					pipe([image, image]    ,     prompt=[prompt, prompt]					)

 @slow
 @require_tf
 def    __snake_case   (     self    :						str					)	->							Dict:
        _UpperCamelCase       :    Any					  =					pipeline('image-to-text'    ,     model='ydshieh/vit-gpt2-coco-en'    ,     framework='tf'					)
        _UpperCamelCase       :    int					  =					'./tests/fixtures/tests_samples/COCO/000000039769.png'

        _UpperCamelCase       :    Tuple					  =					pipe(lowerCamelCase					)
        self.assertEqual(lowerCamelCase    ,     [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]					)

        _UpperCamelCase       :    Dict					  =					pipe([image, image]					)
        self.assertEqual(
            lowerCamelCase    ,     [
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
            ]    ,     )




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



