






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[int]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__="attention"    )			->							List[str]:
  UpperCamelCase_:   Union[str, Any]        =							np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]    )
  UpperCamelCase_:   Union[str, Any]        =							k_tmp.reshape(k_tmp.shape[0]      ,       k_tmp.shape[1] * k_tmp.shape[2]    )
  UpperCamelCase_:   str        =							np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]    )
  UpperCamelCase_:   Optional[int]        =							o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,       o_tmp.shape[2]    )
  UpperCamelCase_:   int        =							np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]    )
  UpperCamelCase_:   List[str]        =							q_tmp.reshape(q_tmp.shape[0]      ,       q_tmp.shape[1] * q_tmp.shape[2]    )
  UpperCamelCase_:   Dict        =							np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]    )
  UpperCamelCase_:   List[str]        =							v_tmp.reshape(v_tmp.shape[0]      ,       v_tmp.shape[1] * v_tmp.shape[2]    )
  return k, o, q, v

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=False    )			->							List[str]:
  if split_mlp_wi:
    UpperCamelCase_:   List[str]        =							params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    UpperCamelCase_:   Tuple        =							params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    UpperCamelCase_:   Any        =							(wi_a, wi_a)
  else:
    UpperCamelCase_:   Any        =							params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  UpperCamelCase_:   int        =							params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]

def     snake_case   (UpperCAmelCase__      ,       *, UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__ = False    )			->							List[Any]:
  UpperCamelCase_:   List[Any]        =							traverse_util.flatten_dict(variables['target']    )
  UpperCamelCase_:   Union[str, Any]        =							{'/'.join(UpperCAmelCase__    ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  UpperCamelCase_:   Union[str, Any]        =							'encoder/encoder/mlp/wi_0/kernel' in old
  print('Split MLP:'      ,       UpperCAmelCase__    )

  UpperCamelCase_:   Optional[Any]        =							collections.OrderedDict()

  # Shared embeddings.
  UpperCamelCase_:   Dict        =							old['token_embedder/embedding']

  # Encoder.
  for i in range(UpperCAmelCase__    ):
    # Block i, layer 0 (Self Attention).
    UpperCamelCase_:   Optional[Any]        =							tax_layer_norm_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'encoder'      ,       'pre_attention_layer_norm'    )
    UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Dict        =							tax_attention_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'encoder'      ,       'attention'    )
    UpperCamelCase_:   Any        =							layer_norm
    UpperCamelCase_:   Tuple        =							k.T
    UpperCamelCase_:   int        =							o.T
    UpperCamelCase_:   Optional[int]        =							q.T
    UpperCamelCase_:   str        =							v.T

    # Block i, layer 1 (MLP).
    UpperCamelCase_:   int        =							tax_layer_norm_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'encoder'      ,       'pre_mlp_layer_norm'    )
    UpperCamelCase_ ,UpperCamelCase_:   Dict        =							tax_mlp_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'encoder'      ,       UpperCAmelCase__    )
    UpperCamelCase_:   int        =							layer_norm
    if split_mlp_wi:
      UpperCamelCase_:   Optional[int]        =							wi[0].T
      UpperCamelCase_:   Dict        =							wi[1].T
    else:
      UpperCamelCase_:   Optional[Any]        =							wi.T
    UpperCamelCase_:   int        =							wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      UpperCamelCase_:   int        =							tax_relpos_bias_lookup(
          UpperCAmelCase__      ,       UpperCAmelCase__      ,       'encoder'    ).T

  UpperCamelCase_:   int        =							old['encoder/encoder_norm/scale']

  if not scalable_attention:
    UpperCamelCase_:   int        =							tax_relpos_bias_lookup(
        UpperCAmelCase__      ,       0      ,       'encoder'    ).T
    UpperCamelCase_:   List[str]        =							tax_relpos_bias_lookup(
        UpperCAmelCase__      ,       0      ,       'decoder'    ).T

  if not is_encoder_only:
    # Decoder.
    for i in range(UpperCAmelCase__    ):
      # Block i, layer 0 (Self Attention).
      UpperCamelCase_:   Dict        =							tax_layer_norm_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'      ,       'pre_self_attention_layer_norm'    )
      UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Any        =							tax_attention_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'      ,       'self_attention'    )
      UpperCamelCase_:   str        =							layer_norm
      UpperCamelCase_:   Tuple        =							k.T
      UpperCamelCase_:   List[Any]        =							o.T
      UpperCamelCase_:   Optional[Any]        =							q.T
      UpperCamelCase_:   Optional[Any]        =							v.T

      # Block i, layer 1 (Cross Attention).
      UpperCamelCase_:   Tuple        =							tax_layer_norm_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'      ,       'pre_cross_attention_layer_norm'    )
      UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							tax_attention_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'      ,       'encoder_decoder_attention'    )
      UpperCamelCase_:   Optional[Any]        =							layer_norm
      UpperCamelCase_:   Union[str, Any]        =							k.T
      UpperCamelCase_:   Dict        =							o.T
      UpperCamelCase_:   Dict        =							q.T
      UpperCamelCase_:   Any        =							v.T

      # Block i, layer 2 (MLP).
      UpperCamelCase_:   Union[str, Any]        =							tax_layer_norm_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'      ,       'pre_mlp_layer_norm'    )
      UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							tax_mlp_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'      ,       UpperCAmelCase__    )
      UpperCamelCase_:   Dict        =							layer_norm
      if split_mlp_wi:
        UpperCamelCase_:   Tuple        =							wi[0].T
        UpperCamelCase_:   List[str]        =							wi[1].T
      else:
        UpperCamelCase_:   Any        =							wi.T
      UpperCamelCase_:   Tuple        =							wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        UpperCamelCase_:   int        =							tax_relpos_bias_lookup(UpperCAmelCase__      ,       UpperCAmelCase__      ,       'decoder'    ).T

    UpperCamelCase_:   Any        =							old['decoder/decoder_norm/scale']

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      UpperCamelCase_:   Union[str, Any]        =							old['decoder/logits_dense/kernel'].T

  return new

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  UpperCamelCase_:   Union[str, Any]        =							collections.OrderedDict([(k, torch.from_numpy(v.copy()    )) for (k, v) in converted_params.items()]    )

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    UpperCamelCase_:   str        =							state_dict['shared.weight']

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      UpperCamelCase_:   List[Any]        =							state_dict['shared.weight']

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print('Using shared word embeddings as lm_head.'    )
      UpperCamelCase_:   str        =							state_dict['shared.weight']

  return state_dict

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[Any]:
  UpperCamelCase_:   Any        =							checkpoints.load_tax_checkpoint(UpperCAmelCase__    )
  UpperCamelCase_:   int        =							convert_tax_to_pytorch(
      UpperCAmelCase__      ,       num_layers=config.num_layers      ,       is_encoder_only=UpperCAmelCase__      ,       scalable_attention=UpperCAmelCase__    )
  UpperCamelCase_:   Tuple        =							make_state_dict(UpperCAmelCase__      ,       UpperCAmelCase__    )
  model.load_state_dict(UpperCAmelCase__      ,       strict=UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__ = False      ,       UpperCAmelCase__ = False      ,       )			->							Any:
  UpperCamelCase_:   Any        =							MTaConfig.from_json_file(UpperCAmelCase__    )
  print(F'''Building PyTorch model from configuration: {config}'''    )
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    UpperCamelCase_:   Optional[Any]        =							UMTaEncoderModel(UpperCAmelCase__    )
  else:
    UpperCamelCase_:   Dict        =							UMTaForConditionalGeneration(UpperCAmelCase__    )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(UpperCAmelCase__    )

  # Verify that we can load the checkpoint.
  model.from_pretrained(UpperCAmelCase__    )
  print('Done'    )


if __name__ == "__main__":
   A_						:						List[str]						      =					argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
   # Required parameters
   parser.add_argument(
       '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
   )
   parser.add_argument(
       '--scalable_attention',
       action='store_true',
       help='Whether the model uses scaled attention (umt5 model)',
       default=False,
   )
   A_						:						int						      =					parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path,
       args.config_file,
       args.pytorch_dump_path,
       args.is_encoder_only,
       args.scalable_attention,
   )

"""simple docstring"""




from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests



def 							_lowerCamelCase			(			__a = "isbn/0140328726"     ):
		SCREAMING_SNAKE_CASE_    			=			olid.strip().strip('''/'''     )  # Remove leading/trailing whitespace & slashes
		if new_olid.count('''/'''     ) != 1:
				SCREAMING_SNAKE_CASE_    			=			F'{olid} is not a valid Open Library olid'
				raise ValueError(__a     )
		return requests.get(F'https://openlibrary.org/{new_olid}.json'     ).json()



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			{
		    '''title''': '''Title''',
		    '''publish_date''': '''Publish date''',
		    '''authors''': '''Authors''',
		    '''number_of_pages''': '''Number of pages:''',
		    '''first_sentence''': '''First sentence''',
		    '''isbn_10''': '''ISBN (10)''',
		    '''isbn_13''': '''ISBN (13)''',
		}
		SCREAMING_SNAKE_CASE_    			=			{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
		SCREAMING_SNAKE_CASE_    			=			[
		    get_openlibrary_data(author['''key''']     )['''name'''] for author in data['''Authors''']
		]
		SCREAMING_SNAKE_CASE_    			=			data['''First sentence''']['''value''']
		for key, value in data.items():
				if isinstance(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			''', '''.join(__a     )
		return data


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						while True:
												lowerCAmelCase__				=					input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
												if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																		break

												if len(isbn) not in (10, 13) or not isbn.isdigit():
																		print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
																		continue

												print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

												try:
																		lowerCAmelCase__				=					summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
																		print('\n'.join(f'''{key}: {value}''' for key, value in book_summary.items()))
												except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																		print(f'''Sorry, there are no results for ISBN: {isbn}.''')