




import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch





class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    @patch("sys.argv"   ,	["fakeprogrampath", "env"]   )
    def    a      (       self     :     str   )      ->       List[str]:
         # test transformers-cli env
         import transformers.commands.transformers_cli

         with CaptureStd() as cs:
              transformers.commands.transformers_cli.main()
         self.assertIn("Python version"   ,	cs.out   )
         self.assertIn("Platform"   ,	cs.out   )
         self.assertIn("Using distributed or parallel set-up in script?"   ,	cs.out   )


    @is_pt_tf_cross_test
    @patch(
        "sys.argv"   ,	["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
    def    a      (       self     :     Union[str, Any]   )      ->       List[Any]:
         import transformers.commands.transformers_cli

         shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"   ,	ignore_errors=SCREAMING_SNAKE_CASE__   )  # cleans potential past runs
         transformers.commands.transformers_cli.main()

         # The original repo has no TF weights -- if they exist, they were created by the CLI
         self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )


    @require_torch
    @patch("sys.argv"   ,	["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
    def    a      (       self     :     Optional[Any]   )      ->       Any:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"   ,	ignore_errors=SCREAMING_SNAKE_CASE__   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )


    @require_torch
    @patch(
        "sys.argv"   ,	[
            "fakeprogrampath",
            "download",
            "hf-internal-testing/test_dynamic_model_with_tokenizer",
            "--trust-remote-code",
            "--cache-dir",
            "/tmp",
        ]   ,	)
    def    a      (       self     :     Optional[int]   )      ->       List[Any]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"   ,	ignore_errors=SCREAMING_SNAKE_CASE__   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
         self.assertTrue(
             os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )






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 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     lowerCAmelCase__		= checkpoint_dict["target"]

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

     lowerCAmelCase__		= state_dict["fnet.embeddings.position_ids"]
     lowerCAmelCase__		= {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]			)

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

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

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

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

     # Pooler Layers
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]			)

     # Masked LM Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]			)
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]			)

     # Seq Relationship Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]			)
     lowerCAmelCase__		= 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__":
     UpperCamelCase								=     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.')
     UpperCamelCase								=     parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
