
"""simple docstring"""

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:
   SCREAMING_SNAKE_CASE_         =  False


if is_vision_available():
   from PIL import Image
else:
   class        _UpperCAmelCase							:




         @staticmethod
         def       a_ (							*lowercase_				,    **lowercase_							)  ->   List[str]:
             pass
@is_pipeline_test
@require_vision
class        _UpperCAmelCase							(						unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		MODEL_FOR_VISION_2_SEQ_MAPPING
      __SCREAMING_SNAKE_CASE				:							int							 =		TF_MODEL_FOR_VISION_2_SEQ_MAPPING




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model=lowercase_				,    tokenizer=lowercase_				,    image_processor=lowercase_							)
          UpperCAmelCase       =   [
              Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
              './tests/fixtures/tests_samples/COCO/000000039769.png',
          ]
          return pipe, examples




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   pipe(lowercase_							)
          self.assertEqual(
              lowercase_				,    [
                  [{'generated_text': ANY(lowercase_							)}],
                  [{'generated_text': ANY(lowercase_							)}],
              ]				,    )




      @require_tf
      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='hf-internal-testing/tiny-random-vit-gpt2'				,    framework='tf'							)
          UpperCAmelCase       =   './tests/fixtures/tests_samples/COCO/000000039769.png'

          UpperCAmelCase       =   pipe(lowercase_							)
          self.assertEqual(
              lowercase_				,    [
                  {
                      'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                  },
              ]				,    )

          UpperCAmelCase       =   pipe([image, image]							)
          self.assertEqual(
              lowercase_				,    [
                  [
                      {
                          'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                      }
                  ],
                  [
                      {
                          'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                      }
                  ],
              ]				,    )

          UpperCAmelCase       =   pipe(lowercase_				,    max_new_tokens=1							)
          self.assertEqual(
              lowercase_				,    [{'generated_text': 'growth'}]				,    )




      @require_torch
      def       a_ (							self							)  ->   str:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='hf-internal-testing/tiny-random-vit-gpt2'							)
          UpperCAmelCase       =   './tests/fixtures/tests_samples/COCO/000000039769.png'

          UpperCAmelCase       =   pipe(lowercase_							)
          self.assertEqual(
              lowercase_				,    [
                  {
                      'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                  },
              ]				,    )

          UpperCAmelCase       =   pipe([image, image]							)
          self.assertEqual(
              lowercase_				,    [
                  [
                      {
                          'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                      }
                  ],
                  [
                      {
                          'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                      }
                  ],
              ]				,    )




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

          UpperCAmelCase       =   pipe(lowercase_				,    prompt=lowercase_							)
          self.assertTrue(outputs[0]['generated_text'].startswith(lowercase_							)							)




      @slow
      @require_torch
      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='ydshieh/vit-gpt2-coco-en'							)
          UpperCAmelCase       =   './tests/fixtures/tests_samples/COCO/000000039769.png'

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

          UpperCAmelCase       =   pipe([image, image]							)
          self.assertEqual(
              lowercase_				,    [
                  [{'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       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='Salesforce/blip-image-captioning-base'							)
          UpperCAmelCase       =   'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
          UpperCAmelCase       =   Image.open(requests.get(lowercase_				,    stream=lowercase_							).raw							)

          UpperCAmelCase       =   pipe(lowercase_							)
          self.assertEqual(lowercase_				,    [{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]							)




      @slow
      @require_torch
      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='microsoft/git-base-coco'							)
          UpperCAmelCase       =   'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
          UpperCAmelCase       =   Image.open(requests.get(lowercase_				,    stream=lowercase_							).raw							)

          UpperCAmelCase       =   pipe(lowercase_							)
          self.assertEqual(lowercase_				,    [{'generated_text': 'a cartoon of a purple character.'}]							)




      @slow
      @require_torch
      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='Salesforce/blip-image-captioning-base'							)
          UpperCAmelCase       =   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
          UpperCAmelCase       =   Image.open(requests.get(lowercase_				,    stream=lowercase_							).raw							)

          UpperCAmelCase       =   'a photography of'

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

          with self.assertRaises(lowercase_							):
              UpperCAmelCase       =   pipe([image, image]				,    prompt=[prompt, prompt]							)




      @slow
      @require_torch
      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='microsoft/git-base-coco'							)
          UpperCAmelCase       =   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
          UpperCAmelCase       =   Image.open(requests.get(lowercase_				,    stream=lowercase_							).raw							)

          UpperCAmelCase       =   'a photo of a'

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

          with self.assertRaises(lowercase_							):
              UpperCAmelCase       =   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       a_ (							self							)  ->   Any:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='google/pix2struct-ai2d-base'							)
          UpperCAmelCase       =   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
          UpperCAmelCase       =   Image.open(requests.get(lowercase_				,    stream=lowercase_							).raw							)

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

          UpperCAmelCase       =   pipe(lowercase_				,    prompt=lowercase_							)
          self.assertEqual(lowercase_				,    [{'generated_text': 'ash cloud'}]							)

          with self.assertRaises(lowercase_							):
              UpperCAmelCase       =   pipe([image, image]				,    prompt=[prompt, prompt]							)




      @slow
      @require_tf
      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   pipeline('image-to-text'				,    model='ydshieh/vit-gpt2-coco-en'				,    framework='tf'							)
          UpperCAmelCase       =   './tests/fixtures/tests_samples/COCO/000000039769.png'

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

          UpperCAmelCase       =   pipe([image, image]							)
          self.assertEqual(
              lowercase_				,    [
                  [{'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 '}],
              ]				,    )





"""simple docstring"""

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

    # Embeddings
    # Position IDs
    UpperCAmelCase       =   fnet_pretraining_model.state_dict()

    UpperCAmelCase       =   state_dict['fnet.embeddings.position_ids']
    UpperCAmelCase       =   {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']	)

    # Pooler Layers
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

    # Masked LM Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']	)
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_bias']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(lowerCAmelCase	)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--fnet_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained FNet model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



