





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Tuple=None       )      ->				Optional[int]:
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __snake_case							       =   nn.Parameter(snake_case_       )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      __snake_case							       =   nn.Parameter(snake_case_       )






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Dict       )      ->				Dict:
   # set torch weights for 1-to-1 comparison
   __snake_case							       =   np.asarray(weights[0]       )
   __snake_case							       =   np.asarray(weights[1]       )
   __snake_case							       =   np.asarray(weights[2]       )

   set_param(
       torch_layer.self_attention.query_key       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.value       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.output.dense       ,      torch.tensor(snake_case_       ).view(-1       ,      snake_case_       ).contiguous().transpose(0       ,      1       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Dict       )      ->				Union[str, Any]:
   # set torch weights for 1-to-1 comparison
   __snake_case							       =   np.asarray(weights[0]       )
   __snake_case							       =   np.asarray(weights[1]       )
   __snake_case							       =   np.asarray(weights[2]       )
   __snake_case							       =   np.asarray(weights[3]       )

   set_param(
       torch_layer.self_attention.query       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.key       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.self_attention.value       ,      torch.tensor(snake_case_       ).transpose(1       ,      2       ).contiguous().view(-1       ,      snake_case_       )       ,      )
   set_param(
       torch_layer.output.dense       ,      torch.tensor(snake_case_       ).view(-1       ,      snake_case_       ).contiguous().transpose(0       ,      1       )       ,      )






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  int       ,      snake_case_	:  Optional[Any]       )      ->				List[Any]:
   # layernorm 1
   __snake_case							       =   weights[0][0][0]
   __snake_case							       =   np.asarray(layer_norm_a[0]       )
   __snake_case							       =   np.asarray(layer_norm_a[1]       )
   set_param(
       torch_block.attention.layer_norm       ,      torch.tensor(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # lsh weights + output
   __snake_case							       =   weights[0][1]
   if len(snake_case_       ) < 4:
      set_layer_weights_in_torch_lsh(snake_case_       ,      torch_block.attention       ,      snake_case_       )
   else:
      set_layer_weights_in_torch_local(snake_case_       ,      torch_block.attention       ,      snake_case_       )

   # intermediate weighs
   __snake_case							       =   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(snake_case_       ) == 4:
      __snake_case							       =   intermediate_weights[2]

   # layernorm 2
   __snake_case							       =   np.asarray(intermediate_weights[0][0]       )
   __snake_case							       =   np.asarray(intermediate_weights[0][1]       )
   set_param(
       torch_block.feed_forward.layer_norm       ,      torch.tensor(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # intermediate dense
   __snake_case							       =   np.asarray(intermediate_weights[1][0]       )
   __snake_case							       =   np.asarray(intermediate_weights[1][1]       )
   set_param(
       torch_block.feed_forward.dense.dense       ,      torch.tensor(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )

   # intermediate out
   __snake_case							       =   np.asarray(intermediate_weights[4][0]       )
   __snake_case							       =   np.asarray(intermediate_weights[4][1]       )
   set_param(
       torch_block.feed_forward.output.dense       ,      torch.tensor(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Union[str, Any]       )      ->				str:
   # reformer model
   __snake_case							       =   torch_model.reformer

   # word embeds
   __snake_case							       =   np.asarray(weights[1]       )
   set_param(
       torch_model_reformer.embeddings.word_embeddings       ,      torch.tensor(snake_case_       )       ,      )

   if isinstance(weights[3]       ,      snake_case_       ):
      __snake_case							       =   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights       )       ):
         __snake_case							       =   np.asarray(weights[3][emb_idx][0]       )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         __snake_case							       =   nn.Parameter(torch.tensor(snake_case_       )       )

   __snake_case							       =   weights[5]
   assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
       snake_case_       ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers       ):
      __snake_case							       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(snake_case_       ,      snake_case_       ,      snake_case_       )

   # output layer norm
   __snake_case							       =   np.asarray(weights[7][0]       )
   __snake_case							       =   np.asarray(weights[7][1]       )
   set_param(
       torch_model_reformer.encoder.layer_norm       ,      torch.tensor(snake_case_       )       ,      torch.tensor(snake_case_       )       ,      )

   # output embeddings
   __snake_case							       =   np.asarray(weights[9][0]       )
   __snake_case							       =   np.asarray(weights[9][1]       )
   set_param(
       torch_model.lm_head.decoder       ,      torch.tensor(snake_case_       ).transpose(0       ,      1       ).contiguous()       ,      torch.tensor(snake_case_       )       ,      )






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  Tuple       )      ->				Tuple:
   # Initialise PyTorch model
   __snake_case							       =   ReformerConfig.from_json_file(snake_case_       )
   print(f"""Building PyTorch model from configuration: {config}"""       )
   __snake_case							       =   ReformerModelWithLMHead(snake_case_       )

   with open(snake_case_       ,      '''rb'''       ) as f:
      __snake_case							       =   pickle.load(snake_case_       )['''weights''']

   set_model_weights_in_torch(snake_case_       ,      snake_case_       ,      config.hidden_size       )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
   torch.save(model.state_dict()       ,      snake_case_       )


if __name__ == "__main__":
    snake_case_				=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained Reformer model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    snake_case_				=			parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)








import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      List[Any]				,			a__       :      Dict=13				,			a__       :      str=32				,			a__       :      Tuple=3				,			a__       :      Optional[Any]=4				,			a__       :      Optional[int]=[10, 20, 30, 40]				,			a__       :      List[Any]=[2, 2, 3, 2]				,			a__       :      List[Any]=True				,			a__       :      int=True				,			a__       :      List[Any]=37				,			a__       :      Any="gelu"				,			a__       :      int=10				,			a__       :      Dict=0.0_2				,			a__       :      Dict=["stage2", "stage3", "stage4"]				,			a__       :      Tuple=[2, 3, 4]				,			a__       :      List[str]=None				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   image_size
         __snake_case							       =   num_channels
         __snake_case							       =   num_stages
         __snake_case							       =   hidden_sizes
         __snake_case							       =   depths
         __snake_case							       =   is_training
         __snake_case							       =   use_labels
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   num_labels
         __snake_case							       =   initializer_range
         __snake_case							       =   out_features
         __snake_case							       =   out_indices
         __snake_case							       =   scope


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __snake_case							       =   None
         if self.use_labels:
            __snake_case							       =   ids_tensor([self.batch_size]				,			self.num_labels      )

         __snake_case							       =   self.get_config()
         return config, pixel_values, labels


      def    a (self       :      List[str]      ):





         """simple docstring"""



         return ConvNextConfig(
             num_channels=self.num_channels				,			hidden_sizes=self.hidden_sizes				,			depths=self.depths				,			num_stages=self.num_stages				,			hidden_act=self.hidden_act				,			is_decoder=a__				,			initializer_range=self.initializer_range				,			out_features=self.out_features				,			out_indices=self.out_indices				,			num_labels=self.num_labels				,			)


      def    a (self       :      str				,			a__       :      Union[str, Any]				,			a__       :      List[str]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__      )
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape				,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,			)


      def    a (self       :      Optional[Any]				,			a__       :      List[Any]				,			a__       :      str				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextForImageClassification(a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			labels=a__      )
         self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)      )


      def    a (self       :      Tuple				,			a__       :      List[Any]				,			a__       :      List[str]				,			a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextBackbone(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__      )

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps      )				,			len(config.out_features      )      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      )				,			[self.batch_size, self.hidden_sizes[1], 4, 4]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      )				,			len(config.out_features      )      )
         self.parent.assertListEqual(model.channels				,			config.hidden_sizes[1:]      )

         # verify backbone works with out_features=None
         __snake_case							       =   None
         __snake_case							       =   ConvNextBackbone(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__      )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps      )				,			1      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      )				,			[self.batch_size, self.hidden_sizes[-1], 1, 1]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      )				,			1      )
         self.parent.assertListEqual(model.channels				,			[config.hidden_sizes[-1]]      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         __snake_case       ,	__snake_case       ,	__snake_case							       =   config_and_inputs
         __snake_case							       =   {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Dict							=    (
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Optional[Any]							=    (
          {'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      A_						:				Dict							=    True
      A_						:				Optional[Any]							=    False
      A_						:				int							=    False
      A_						:				int							=    False
      A_						:				List[str]							=    False


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			has_text_modality=a__				,			hidden_size=37      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


      def    a (self       :      str      ):





         """simple docstring"""



         return


      @unittest.skip(reason='''ConvNext does not use inputs_embeds'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip(reason='''ConvNext does not support input and output embeddings'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         pass


      @unittest.skip(reason='''ConvNext does not use feedforward chunking'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   model_class(a__      )
            __snake_case							       =   inspect.signature(model.forward      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __snake_case							       =   [*signature.parameters.keys()]

            __snake_case							       =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,			a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         def check_hidden_states_output(a__       :      List[str]				,			a__       :      str				,			a__       :      Tuple      ):
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()

            with torch.no_grad():
               __snake_case							       =   model(**self._prepare_for_class(a__				,			a__      )      )

            __snake_case							       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            __snake_case							       =   self.model_tester.num_stages
            self.assertEqual(len(a__      )				,			expected_num_stages + 1      )

            # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )				,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,			)

         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   True
            check_hidden_states_output(a__				,			a__				,			a__      )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __snake_case							       =   True

            check_hidden_states_output(a__				,			a__				,			a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__      )


      @slow
      def    a (self       :      Any      ):





         """simple docstring"""



         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   ConvNextModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






def lowerCamelCase__    (							)      ->				List[str]:
   __snake_case							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
   return image

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @cached_property
      def    a (self       :      Tuple      ):





         """simple docstring"""



         return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''      ) if is_vision_available() else None


      @slow
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''      ).to(a__      )

         __snake_case							       =   self.default_image_processor
         __snake_case							       =   prepare_img()
         __snake_case							       =   image_processor(images=a__				,			return_tensors='''pt'''      ).to(a__      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(**a__      )

         # verify the logits
         __snake_case							       =   torch.Size((1, 1000)      )
         self.assertEqual(outputs.logits.shape				,			a__      )

         __snake_case							       =   torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]      ).to(a__      )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			a__				,			atol=1E-4      )      )






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase						,      _UpperCAmelCase  ):
      A_						:				Union[str, Any]							=    (ConvNextBackbone,) if is_torch_available() else ()
      A_						:				List[Any]							=    ConvNextConfig

      A_						:				Optional[Any]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextModelTester(self      )

