
'''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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None			):







 """simple docstring"""


 assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
 lowerCAmelCase__     :   Union[str, Any]     		=	nn.Parameter(lowerCamelCase_			)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
  lowerCAmelCase__     :   List[str]     		=	nn.Parameter(lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	np.asarray(weights[0]			)
 lowerCAmelCase__     :   Any     		=	np.asarray(weights[1]			)
 lowerCAmelCase__     :   List[str]     		=	np.asarray(weights[2]			)

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	np.asarray(weights[0]			)
 lowerCAmelCase__     :   str     		=	np.asarray(weights[1]			)
 lowerCAmelCase__     :   Union[str, Any]     		=	np.asarray(weights[2]			)
 lowerCAmelCase__     :   Union[str, Any]     		=	np.asarray(weights[3]			)

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	weights[0][0][0]
 lowerCAmelCase__     :   str     		=	np.asarray(layer_norm_a[0]			)
 lowerCAmelCase__     :   str     		=	np.asarray(layer_norm_a[1]			)
 set_param(
     torch_block.attention.layer_norm      ,			torch.tensor(lowerCamelCase_			)      ,			torch.tensor(lowerCamelCase_			)      ,			)

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

 # intermediate weighs
 lowerCAmelCase__     :   int     		=	weights[2][0][1][2]

 # Chunked Feed Forward
 if len(lowerCamelCase_			) == 4:
  lowerCAmelCase__     :   List[Any]     		=	intermediate_weights[2]

 # layernorm 2
 lowerCAmelCase__     :   Optional[Any]     		=	np.asarray(intermediate_weights[0][0]			)
 lowerCAmelCase__     :   Tuple     		=	np.asarray(intermediate_weights[0][1]			)
 set_param(
     torch_block.feed_forward.layer_norm      ,			torch.tensor(lowerCamelCase_			)      ,			torch.tensor(lowerCamelCase_			)      ,			)

 # intermediate dense
 lowerCAmelCase__     :   Union[str, Any]     		=	np.asarray(intermediate_weights[1][0]			)
 lowerCAmelCase__     :   Dict     		=	np.asarray(intermediate_weights[1][1]			)
 set_param(
     torch_block.feed_forward.dense.dense      ,			torch.tensor(lowerCamelCase_			).transpose(0      ,			1			).contiguous()      ,			torch.tensor(lowerCamelCase_			)      ,			)

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	torch_model.reformer

 # word embeds
 lowerCAmelCase__     :   str     		=	np.asarray(weights[1]			)
 set_param(
     torch_model_reformer.embeddings.word_embeddings      ,			torch.tensor(lowerCamelCase_			)      ,			)

 if isinstance(weights[3]      ,			lowerCamelCase_			):
  lowerCAmelCase__     :   List[str]     		=	torch_model_reformer.embeddings.position_embeddings
  for emb_idx in range(len(position_embeddings.weights			)			):
   lowerCAmelCase__     :   str     		=	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'''
   lowerCAmelCase__     :   Tuple     		=	nn.Parameter(torch.tensor(lowerCamelCase_			)			)

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

 # output layer norm
 lowerCAmelCase__     :   List[str]     		=	np.asarray(weights[7][0]			)
 lowerCAmelCase__     :   str     		=	np.asarray(weights[7][1]			)
 set_param(
     torch_model_reformer.encoder.layer_norm      ,			torch.tensor(lowerCamelCase_			)      ,			torch.tensor(lowerCamelCase_			)      ,			)

 # output embeddings
 lowerCAmelCase__     :   Any     		=	np.asarray(weights[9][0]			)
 lowerCAmelCase__     :   Dict     		=	np.asarray(weights[9][1]			)
 set_param(
     torch_model.lm_head.decoder      ,			torch.tensor(lowerCamelCase_			).transpose(0      ,			1			).contiguous()      ,			torch.tensor(lowerCamelCase_			)      ,			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	ReformerConfig.from_json_file(lowerCamelCase_			)
 print(f'''Building PyTorch model from configuration: {config}'''			)
 lowerCAmelCase__     :   Union[str, Any]     		=	ReformerModelWithLMHead(lowerCamelCase_			)

 with open(lowerCamelCase_      ,			"rb"			) as f:
  lowerCAmelCase__     :   Any     		=	pickle.load(lowerCamelCase_			)["weights"]

 set_model_weights_in_torch(lowerCamelCase_      ,			lowerCamelCase_      ,			config.hidden_size			)

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


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)







'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





