'''simple docstring'''
import argparse
import os
import re


a=  '''src/transformers'''

# Pattern that looks at the indentation in a line.
a=  re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
a=  re.compile(r'''^\s*\"([^\"]+)\":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a=  re.compile(r'''^\s*_import_structure\[\"([^\"]+)\"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
a=  re.compile(r'''^\s*\"([^\"]+)\",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a=  re.compile(r'''\[([^\]]+)\]''')







def  _UpperCamelCase (		_a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  _re_indent.search(_a			)
    return "" if search is None else search.groups()[0]







def  _UpperCamelCase (		_a	:	List[str]				,     _a	:	Dict=""				,     _a	:	Dict=None				,     _a	:	Dict=None			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  0
    __UpperCamelCase	:	int		    =  code.split('\n'			)
    if start_prompt is not None:
        while not lines[index].startswith(_a			):
            index += 1
        __UpperCamelCase	:	List[Any]		    =  ['''\n'''.join(lines[:index]			)]
    else:
        __UpperCamelCase	:	int		    =  []

    # We split into blocks until we get to the `end_prompt` (or the end of the block).
    __UpperCamelCase	:	Dict		    =  [lines[index]]
    index += 1
    while index < len(_a			) and (end_prompt is None or not lines[index].startswith(_a			)):
        if len(lines[index]			) > 0 and get_indent(lines[index]			) == indent_level:
            if len(_a			) > 0 and get_indent(current_block[-1]			).startswith(indent_level + ' '			):
                current_block.append(lines[index]			)
                blocks.append('\n'.join(_a			)			)
                if index < len(_a			) - 1:
                    __UpperCamelCase	:	Optional[int]		    =  [lines[index + 1]]
                    index += 1
                else:
                    __UpperCamelCase	:	Union[str, Any]		    =  []
            else:
                blocks.append('\n'.join(_a			)			)
                __UpperCamelCase	:	Dict		    =  [lines[index]]
        else:
            current_block.append(lines[index]			)
        index += 1

    # Adds current block if it's nonempty.
    if len(_a			) > 0:
        blocks.append('\n'.join(_a			)			)

    # Add final block after end_prompt if provided.
    if end_prompt is not None and index < len(_a			):
        blocks.append('\n'.join(lines[index:]			)			)

    return blocks







def  _UpperCamelCase (		_a	:	Optional[Any]			):




    """simple docstring"""



    def _inner(_a	:	List[Any]			):
        return key(_a			).lower().replace('_'				,     ''			)

    return _inner







def  _UpperCamelCase (		_a	:	Tuple				,     _a	:	Optional[int]=None			):




    """simple docstring"""



    def noop(_a	:	Optional[Any]			):
        return x

    if key is None:
        __UpperCamelCase	:	Any		    =  noop
    # Constants are all uppercase, they go first.
    __UpperCamelCase	:	List[Any]		    =  [obj for obj in objects if key(_a			).isupper()]
    # Classes are not all uppercase but start with a capital, they go second.
    __UpperCamelCase	:	Optional[Any]		    =  [obj for obj in objects if key(_a			)[0].isupper() and not key(_a			).isupper()]
    # Functions begin with a lowercase, they go last.
    __UpperCamelCase	:	Tuple		    =  [obj for obj in objects if not key(_a			)[0].isupper()]

    __UpperCamelCase	:	List[Any]		    =  ignore_underscore(_a			)
    return sorted(_a				,     key=_a			) + sorted(_a				,     key=_a			) + sorted(_a				,     key=_a			)







def  _UpperCamelCase (		_a	:	Tuple			):




    """simple docstring"""



    def _replace(_a	:	int			):
        __UpperCamelCase	:	str		    =  match.groups()[0]
        if "," not in imports:
            return f"""[{imports}]"""
        __UpperCamelCase	:	List[str]		    =  [part.strip().replace('"'				,     ''			) for part in imports.split(','			)]
        # We will have a final empty element if the line finished with a comma.
        if len(keys[-1]			) == 0:
            __UpperCamelCase	:	Tuple		    =  keys[:-1]
        return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(_a			)]			) + "]"

    __UpperCamelCase	:	List[Any]		    =  import_statement.split('\n'			)
    if len(_a			) > 3:
        # Here we have to sort internal imports that are on several lines (one per name):
        # key: [
        #     "object1",
        #     "object2",
        #     ...
        # ]

        # We may have to ignore one or two lines on each side.
        __UpperCamelCase	:	int		    =  2 if lines[1].strip() == '''[''' else 1
        __UpperCamelCase	:	Optional[Any]		    =  [(i, _re_strip_line.search(_a			).groups()[0]) for i, line in enumerate(lines[idx:-idx]			)]
        __UpperCamelCase	:	str		    =  sort_objects(_a				,     key=lambda _a			: x[1]			)
        __UpperCamelCase	:	Tuple		    =  [lines[x[0] + idx] for x in sorted_indices]
        return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]			)
    elif len(_a			) == 3:
        # Here we have to sort internal imports that are on one separate line:
        # key: [
        #     "object1", "object2", ...
        # ]
        if _re_bracket_content.search(lines[1]			) is not None:
            __UpperCamelCase	:	Tuple		    =  _re_bracket_content.sub(_replace				,     lines[1]			)
        else:
            __UpperCamelCase	:	str		    =  [part.strip().replace('"'				,     ''			) for part in lines[1].split(','			)]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]			) == 0:
                __UpperCamelCase	:	int		    =  keys[:-1]
            __UpperCamelCase	:	Optional[int]		    =  get_indent(lines[1]			) + ''', '''.join([f"""\"{k}\"""" for k in sort_objects(_a			)]			)
        return "\n".join(_a			)
    else:
        # Finally we have to deal with imports fitting on one line
        __UpperCamelCase	:	List[Any]		    =  _re_bracket_content.sub(_replace				,     _a			)
        return import_statement







def  _UpperCamelCase (		_a	:	List[Any]				,     _a	:	Tuple=True			):




    """simple docstring"""



    with open(_a				,     encoding='utf-8'			) as f:
        __UpperCamelCase	:	List[str]		    =  f.read()

    if "_import_structure" not in code:
        return

    # Blocks of indent level 0
    __UpperCamelCase	:	int		    =  split_code_in_indented_blocks(
        _a				,     start_prompt='_import_structure = {'				,     end_prompt='if TYPE_CHECKING:'			)

    # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
    for block_idx in range(1				,     len(_a			) - 1			):
        # Check if the block contains some `_import_structure`s thingy to sort.
        __UpperCamelCase	:	str		    =  main_blocks[block_idx]
        __UpperCamelCase	:	Tuple		    =  block.split('\n'			)

        # Get to the start of the imports.
        __UpperCamelCase	:	Optional[Any]		    =  0
        while line_idx < len(_a			) and "_import_structure" not in block_lines[line_idx]:
            # Skip dummy import blocks
            if "import dummy" in block_lines[line_idx]:
                __UpperCamelCase	:	str		    =  len(_a			)
            else:
                line_idx += 1
        if line_idx >= len(_a			):
            continue

        # Ignore beginning and last line: they don't contain anything.
        __UpperCamelCase	:	Any		    =  '''\n'''.join(block_lines[line_idx:-1]			)
        __UpperCamelCase	:	str		    =  get_indent(block_lines[1]			)
        # Slit the internal block into blocks of indent level 1.
        __UpperCamelCase	:	List[str]		    =  split_code_in_indented_blocks(_a				,     indent_level=_a			)
        # We have two categories of import key: list or _import_structure[key].append/extend
        __UpperCamelCase	:	Optional[Any]		    =  _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
        # Grab the keys, but there is a trap: some lines are empty or just comments.
        __UpperCamelCase	:	List[str]		    =  [(pattern.search(_a			).groups()[0] if pattern.search(_a			) is not None else None) for b in internal_blocks]
        # We only sort the lines with a key.
        __UpperCamelCase	:	Optional[Any]		    =  [(i, key) for i, key in enumerate(_a			) if key is not None]
        __UpperCamelCase	:	Optional[Any]		    =  [x[0] for x in sorted(_a				,     key=lambda _a			: x[1]			)]

        # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
        __UpperCamelCase	:	Dict		    =  0
        __UpperCamelCase	:	Tuple		    =  []
        for i in range(len(_a			)			):
            if keys[i] is None:
                reorderded_blocks.append(internal_blocks[i]			)
            else:
                __UpperCamelCase	:	Any		    =  sort_objects_in_import(internal_blocks[sorted_indices[count]]			)
                reorderded_blocks.append(_a			)
                count += 1

        # And we put our main block back together with its first and last line.
        __UpperCamelCase	:	Tuple		    =  '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]			)

    if code != "\n".join(_a			):
        if check_only:
            return True
        else:
            print(f"""Overwriting {file}."""			)
            with open(_a				,     'w'				,     encoding='utf-8'			) as f:
                f.write('\n'.join(_a			)			)







def  _UpperCamelCase (		_a	:	Union[str, Any]=True			):




    """simple docstring"""



    __UpperCamelCase	:	Dict		    =  []
    for root, _, files in os.walk(_a			):
        if "__init__.py" in files:
            __UpperCamelCase	:	int		    =  sort_imports(os.path.join(_a				,     '__init__.py'			)				,     check_only=_a			)
            if result:
                __UpperCamelCase	:	Optional[Any]		    =  [os.path.join(_a				,     '__init__.py'			)]
    if len(_a			) > 0:
        raise ValueError(f"""Would overwrite {len(_a			)} files, run `make style`."""			)


if __name__ == "__main__":
  a=  argparse.ArgumentParser()
  parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
  a=  parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)




'''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 (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



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

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







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[3]			)

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







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

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

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

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[1][1]			)
    set_param(
        torch_block.feed_forward.dense.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )

    # intermediate out
    __UpperCamelCase	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(intermediate_weights[4][1]			)
    set_param(
        torch_block.feed_forward.output.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	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"""
            __UpperCamelCase	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

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

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  pickle.load(_a			)['weights']

    set_model_weights_in_torch(_a				,     _a				,     config.hidden_size			)

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


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)



