





'''simple docstring'''

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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None   )    ->       int:

   '''simple docstring'''







   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   __SCREAMING_SNAKE_CASE   				=							nn.Parameter(__UpperCAmelCase   )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      __SCREAMING_SNAKE_CASE   				=							nn.Parameter(__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[1]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[2]   )

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[1]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[2]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(weights[3]   )

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							weights[0][0][0]
   __SCREAMING_SNAKE_CASE   				=							np.asarray(layer_norm_a[0]   )
   __SCREAMING_SNAKE_CASE   				=							np.asarray(layer_norm_a[1]   )
   set_param(
       torch_block.attention.layer_norm				,						torch.tensor(__UpperCAmelCase   )				,						torch.tensor(__UpperCAmelCase   )				,						)

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

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

   # Chunked Feed Forward
   if len(__UpperCAmelCase   ) == 4:
      __SCREAMING_SNAKE_CASE   				=							intermediate_weights[2]

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

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch_model.reformer

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

   if isinstance(weights[3]				,						__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights   )   ):
         __SCREAMING_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"""
         __SCREAMING_SNAKE_CASE   				=							nn.Parameter(torch.tensor(__UpperCAmelCase   )   )

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

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ReformerConfig.from_json_file(__UpperCAmelCase   )
   print(f"""Building PyTorch model from configuration: {config}"""   )
   __SCREAMING_SNAKE_CASE   				=							ReformerModelWithLMHead(__UpperCAmelCase   )

   with open(__UpperCAmelCase				,						"""rb"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							pickle.load(__UpperCAmelCase   )["""weights"""]

   set_model_weights_in_torch(__UpperCAmelCase				,						__UpperCAmelCase				,						config.hidden_size   )

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


if __name__ == "__main__":
    a   	=    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."
    )
    a   	=    parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""






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 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=13						,			__lowerCAmelCase=32						,			__lowerCAmelCase=3						,			__lowerCAmelCase=4						,			__lowerCAmelCase=[10, 20, 30, 40]						,			__lowerCAmelCase=[2, 2, 3, 2]						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=37						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=10						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=["stage2", "stage3", "stage4"]						,			__lowerCAmelCase=[2, 3, 4]						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

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

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

      def 							A__			(		self		):







            """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=__lowerCAmelCase						,			initializer_range=self.initializer_range						,			out_features=self.out_features						,			out_indices=self.out_indices						,			num_labels=self.num_labels						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      ConvNextModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase		)
            # 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						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      ConvNextBackbone(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase		)

            # 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
            lowercase								=      None
            lowercase								=      ConvNextBackbone(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase		)

            # 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		):







            """simple docstring"""



            lowercase								=      self.prepare_config_and_inputs()
            lowercase				,				lowercase				,				lowercase								=      config_and_inputs
            lowercase								=      {"""pixel_values""": pixel_values}
            return config, inputs_dict






@require_torch
class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  List[str]				    =      (
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      snake_case__     :  Optional[Any]				    =      (
          {'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      snake_case__     :  List[str]				    =      True
      snake_case__     :  Dict				    =      False
      snake_case__     :  Optional[int]				    =      False
      snake_case__     :  Tuple				    =      False
      snake_case__     :  List[str]				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ConvNextModelTester(self		)
            lowercase								=      ConfigTester(self						,			config_class=__lowerCAmelCase						,			has_text_modality=__lowerCAmelCase						,			hidden_size=37		)

      def 							A__			(		self		):







            """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		):







            """simple docstring"""



            return

      @unittest.skip(reason="""ConvNext does not use inputs_embeds"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      @unittest.skip(reason="""ConvNext does not support input and output embeddings"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      @unittest.skip(reason="""ConvNext does not use feedforward chunking"""		)
      def 							A__			(		self		):







            """simple docstring"""



            pass

      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase								=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            def check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):
                  lowercase								=      model_class(__lowerCAmelCase		)
                  model.to(__lowerCAmelCase		)
                  model.eval()

                  with torch.no_grad():
                        lowercase								=      model(**self._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase		)		)

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

                  lowercase								=      self.model_tester.num_stages
                  self.assertEqual(len(__lowerCAmelCase		)						,			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]						,			)

            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowercase								=      True
                  check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

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

                  check_hidden_states_output(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase		)







      @slow
      def 							A__			(		self		):







            """simple docstring"""



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






def      UpperCAmelCase__			(							)					->       List[str]:
      '''simple docstring'''


      lowercase								=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image





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

      @cached_property
      def 							A__			(		self		):







            """simple docstring"""



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







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""		).to(__lowerCAmelCase		)

            lowercase								=      self.default_image_processor
            lowercase								=      prepare_img()
            lowercase								=      image_processor(images=__lowerCAmelCase						,			return_tensors="""pt"""		).to(__lowerCAmelCase		)

            # forward pass
            with torch.no_grad():
                  lowercase								=      model(**__lowerCAmelCase		)

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

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

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






@require_torch
class 					_A		(			unittest.TestCase    ,       lowerCAmelCase      ):
      snake_case__     :  int				    =      (ConvNextBackbone,) if is_torch_available() else ()
      snake_case__     :  Tuple				    =      ConvNextConfig

      snake_case__     :  Dict				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ConvNextModelTester(self		)



