





'''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 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : str=None		)		->  Dict:
    # set parameter of one layer
    assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
    A       :       List[Any]							=						nn.Parameter(UpperCamelCase__		)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
        A       :       Optional[Any]							=						nn.Parameter(UpperCamelCase__		)
def 							_lowerCamelCase(  UpperCamelCase__    : Tuple    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[Any]		)		->  Union[str, Any]:
    # set torch weights for 1-to-1 comparison
    A       :       int							=						np.asarray(weights[0]		)
    A       :       Optional[Any]							=						np.asarray(weights[1]		)
    A       :       Tuple							=						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 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any		)		->  Union[str, Any]:
    # set torch weights for 1-to-1 comparison
    A       :       Optional[Any]							=						np.asarray(weights[0]		)
    A       :       str							=						np.asarray(weights[1]		)
    A       :       Dict							=						np.asarray(weights[2]		)
    A       :       Union[str, Any]							=						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 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Dict		)		->  Union[str, Any]:
    # layernorm 1
    A       :       List[Any]							=						weights[0][0][0]
    A       :       int							=						np.asarray(layer_norm_a[0]		)
    A       :       Optional[Any]							=						np.asarray(layer_norm_a[1]		)
    set_param(
        torch_block.attention.layer_norm    ,       torch.tensor(UpperCamelCase__		)    ,       torch.tensor(UpperCamelCase__		)    ,       )

    # lsh weights + output
    A       :       List[str]							=						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
    A       :       Any							=						weights[2][0][1][2]

    # Chunked Feed Forward
    if len(UpperCamelCase__		) == 4:
        A       :       int							=						intermediate_weights[2]

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

    # intermediate dense
    A       :       str							=						np.asarray(intermediate_weights[1][0]		)
    A       :       List[Any]							=						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
    A       :       Dict							=						np.asarray(intermediate_weights[4][0]		)
    A       :       Tuple							=						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 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]		)		->  Tuple:
    # reformer model
    A       :       Optional[int]							=						torch_model.reformer

    # word embeds
    A       :       Optional[int]							=						np.asarray(weights[1]		)
    set_param(
        torch_model_reformer.embeddings.word_embeddings    ,       torch.tensor(UpperCamelCase__		)    ,       )

    if isinstance(weights[3]    ,       UpperCamelCase__		):
        A       :       Any							=						torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights		)		):
            A       :       List[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'''
            A       :       Optional[Any]							=						nn.Parameter(torch.tensor(UpperCamelCase__		)		)

    A       :       Any							=						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		):
        A       :       Tuple							=						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

    # output layer norm
    A       :       Union[str, Any]							=						np.asarray(weights[7][0]		)
    A       :       Optional[int]							=						np.asarray(weights[7][1]		)
    set_param(
        torch_model_reformer.encoder.layer_norm    ,       torch.tensor(UpperCamelCase__		)    ,       torch.tensor(UpperCamelCase__		)    ,       )

    # output embeddings
    A       :       str							=						np.asarray(weights[9][0]		)
    A       :       Dict							=						np.asarray(weights[9][1]		)
    set_param(
        torch_model.lm_head.decoder    ,       torch.tensor(UpperCamelCase__		).transpose(0    ,       1		).contiguous()    ,       torch.tensor(UpperCamelCase__		)    ,       )
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : List[str]		)		->  Union[str, Any]:
    # Initialise PyTorch model
    A       :       List[str]							=						ReformerConfig.from_json_file(UpperCamelCase__		)
    print(F'''Building PyTorch model from configuration: {config}'''		)
    A       :       Optional[int]							=						ReformerModelWithLMHead(UpperCamelCase__		)

    with open(UpperCamelCase__    ,       '''rb'''		) as f:
        A       :       str							=						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__":
     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'''






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       int							=						[]

    for input_type in input_types:
        if input_type == "text":
            inputs.append('''Text input'''		)
        elif input_type == "image":
            inputs.append(
                Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		).resize((512, 512)		)		)
        elif input_type == "audio":
            inputs.append(torch.ones(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

    for output in outputs:
        if isinstance(UpperCamelCase__    ,       (str, AgentText)		):
            output_types.append('''text'''		)
        elif isinstance(UpperCamelCase__    ,       (Image.Image, AgentImage)		):
            output_types.append('''image'''		)
        elif isinstance(UpperCamelCase__    ,       (torch.Tensor, AgentAudio)		):
            output_types.append('''audio'''		)
        else:
            raise ValueError(F'''Invalid output: {output}'''		)

    return output_types





@is_tool_test
class   _lowercase						:


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''inputs'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''outputs'''					)					)

       A       :       Any							=						self.tool.inputs
       for _input in inputs:
           if isinstance(_input    ,   _UpperCAmelCase					):
               for __input in _input:
                   self.assertTrue(__input in authorized_types					)
           else:
               self.assertTrue(_input in authorized_types					)

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       # There is a single output
       if len(self.tool.outputs					) == 1:
           A       :       Optional[int]							=						[outputs]

       self.assertListEqual(output_types(_UpperCAmelCase					)    ,   self.tool.outputs					)


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''description'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''default_checkpoint'''					)					)
       self.assertTrue(self.tool.description.startswith('''This is a tool that'''					)					)


   def 					snake_case				( self					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

       self.assertEqual(len(_UpperCAmelCase					)    ,   len(self.tool.outputs					)					)

       for output, output_type in zip(_UpperCAmelCase    ,   self.tool.outputs					):
           A       :       Tuple							=						AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       for _input, input_type in zip(_UpperCAmelCase    ,   self.tool.inputs					):
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
           else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)

        # Should not raise an error
       A       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

       self.assertEqual(len(_UpperCAmelCase					)    ,   len(self.tool.outputs					)					)
