




'''simple docstring'''



import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
 import tensorflow as tf

 tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset


lowerCamelCase_	 =		5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Namespace						)				->				int:
   return PTtoTFCommand(
       args.model_name ,  args.local_dir ,  args.max_error ,  args.new_weights ,  args.no_pr ,  args.push ,  args.extra_commit_description ,  args.override_model_class ,  )







class 	lowercase_					(				A     ):


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_    (					__lowerCamelCase      :			ArgumentParser					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   parser.add_parser(
          "pt-to-tf"						,   help=(
              "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
              " Can also be used to validate existing weights without opening PRs, with --no-pr."
          )						,   )
      train_parser.add_argument(
          "--model-name"						,   type=__lowerCamelCase						,   required=__lowerCamelCase						,   help="The model name, including owner/organization, as seen on the hub."						,   )
      train_parser.add_argument(
          "--local-dir"						,   type=__lowerCamelCase						,   default=""						,   help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"						,   )
      train_parser.add_argument(
          "--max-error"						,   type=__lowerCamelCase						,   default=__lowerCamelCase						,   help=(
              F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
          )						,   )
      train_parser.add_argument(
          "--new-weights"						,   action="store_true"						,   help="Optional flag to create new TensorFlow weights, even if they already exist."						,   )
      train_parser.add_argument(
          "--no-pr"						,   action="store_true"						,   help="Optional flag to NOT open a PR with converted weights."					)
      train_parser.add_argument(
          "--push"						,   action="store_true"						,   help="Optional flag to push the weights directly to `main` (requires permissions)"						,   )
      train_parser.add_argument(
          "--extra-commit-description"						,   type=__lowerCamelCase						,   default=""						,   help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)."						,   )
      train_parser.add_argument(
          "--override-model-class"						,   type=__lowerCamelCase						,   default=__lowerCamelCase						,   help="If you think you know better than the auto-detector, you can specify the model class here. "
          "Can be either an AutoModel class or a specific model class like BertForSequenceClassification."						,   )
      train_parser.set_defaults(func=__lowerCamelCase					)



   @staticmethod
   def 							lowerCAmelCase_    (					__lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   set(pt_outputs.keys()					)
      _SCREAMING_SNAKE_CASE =   set(tf_outputs.keys()					)
      if pt_out_attrs != tf_out_attrs:
         raise ValueError(
             F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
             F""" {tf_out_attrs})"""					)

      # 2. For each output attribute, computes the difference
      def _find_pt_tf_differences(__lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			int=""					):
         # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
         # recursivelly, keeping the name of the attribute.
         if isinstance(__lowerCamelCase						,   torch.Tensor					):
            _SCREAMING_SNAKE_CASE =   np.max(np.abs(pt_out.numpy() - tf_out.numpy()					)					)
            _SCREAMING_SNAKE_CASE =   tensor_difference
         else:
            _SCREAMING_SNAKE_CASE =   attr_name
            for i, pt_item in enumerate(__lowerCamelCase					):
               # If it is a named attribute, we keep the name. Otherwise, just its index.
               if isinstance(__lowerCamelCase						,   __lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE =   root_name + pt_item
                  _SCREAMING_SNAKE_CASE =   tf_out[pt_item]
                  _SCREAMING_SNAKE_CASE =   pt_out[pt_item]
               else:
                  _SCREAMING_SNAKE_CASE =   root_name + F"""[{i}]"""
                  _SCREAMING_SNAKE_CASE =   tf_out[i]
               _SCREAMING_SNAKE_CASE =   _find_pt_tf_differences(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)

         return differences

      return _find_pt_tf_differences(__lowerCamelCase						,   __lowerCamelCase						,   {}					)



   def __init__(					self      :			Dict						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			float						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str						,   *__lowerCamelCase      :			List[Any]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   logging.get_logger("transformers-cli/pt_to_tf"					)
      _SCREAMING_SNAKE_CASE =   model_name
      _SCREAMING_SNAKE_CASE =   local_dir if local_dir else os.path.join("/tmp"						,   __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   max_error
      _SCREAMING_SNAKE_CASE =   new_weights
      _SCREAMING_SNAKE_CASE =   no_pr
      _SCREAMING_SNAKE_CASE =   push
      _SCREAMING_SNAKE_CASE =   extra_commit_description
      _SCREAMING_SNAKE_CASE =   override_model_class



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""

      def _get_audio_input():
         _SCREAMING_SNAKE_CASE =   load_dataset("hf-internal-testing/librispeech_asr_dummy"						,   "clean"						,   split="validation"					)
         _SCREAMING_SNAKE_CASE =   ds.sort("id"					).select(range(2					)					)[:2]["audio"]
         _SCREAMING_SNAKE_CASE =   [x["array"] for x in speech_samples]
         return raw_samples

      _SCREAMING_SNAKE_CASE =   type(pt_model.config					)
      if model_config_class in PROCESSOR_MAPPING:
         _SCREAMING_SNAKE_CASE =   AutoProcessor.from_pretrained(self._local_dir					)
         if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
            _SCREAMING_SNAKE_CASE =   processor.tokenizer.eos_token
      elif model_config_class in IMAGE_PROCESSOR_MAPPING:
         _SCREAMING_SNAKE_CASE =   AutoImageProcessor.from_pretrained(self._local_dir					)
      elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
         _SCREAMING_SNAKE_CASE =   AutoFeatureExtractor.from_pretrained(self._local_dir					)
      elif model_config_class in TOKENIZER_MAPPING:
         _SCREAMING_SNAKE_CASE =   AutoTokenizer.from_pretrained(self._local_dir					)
         if processor.pad_token is None:
            _SCREAMING_SNAKE_CASE =   processor.eos_token
      else:
         raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""					)

      _SCREAMING_SNAKE_CASE =   set(inspect.signature(pt_model.forward					).parameters.keys()					)
      _SCREAMING_SNAKE_CASE =   {}
      if "input_ids" in model_forward_signature:
         processor_inputs.update(
             {
                 "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
                 "padding": True,
                 "truncation": True,
             }					)
      if "pixel_values" in model_forward_signature:
         _SCREAMING_SNAKE_CASE =   load_dataset("cifar10"						,   "plain_text"						,   split="test"					)[:2]["img"]
         processor_inputs.update({"images": sample_images}					)
      if "input_features" in model_forward_signature:
         _SCREAMING_SNAKE_CASE =   inspect.signature(processor.feature_extractor					).parameters
         # Pad to the largest input length by default but take feature extractor default
         # padding value if it exists e.g. "max_length" and is not False or None
         if "padding" in feature_extractor_signature:
            _SCREAMING_SNAKE_CASE =   feature_extractor_signature["padding"].default
            if default_strategy is not False and default_strategy is not None:
               _SCREAMING_SNAKE_CASE =   default_strategy
            else:
               _SCREAMING_SNAKE_CASE =   True
         else:
            _SCREAMING_SNAKE_CASE =   True
         processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}					)
      if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
         processor_inputs.update({"audio": _get_audio_input(), "padding": True}					)
      _SCREAMING_SNAKE_CASE =   processor(**__lowerCamelCase						,   return_tensors="pt"					)
      _SCREAMING_SNAKE_CASE =   processor(**__lowerCamelCase						,   return_tensors="tf"					)

      # Extra input requirements, in addition to the input modality
      if (
          config.is_encoder_decoder
          or (hasattr(__lowerCamelCase						,   "encoder"					) and hasattr(__lowerCamelCase						,   "decoder"					))
          or "decoder_input_ids" in tf_dummy_inputs
      ):
         _SCREAMING_SNAKE_CASE =   np.asarray([[1], [1]]						,   dtype=__lowerCamelCase					) * (pt_model.config.decoder_start_token_id or 0)
         pt_input.update({"decoder_input_ids": torch.tensor(__lowerCamelCase					)}					)
         tf_input.update({"decoder_input_ids": tf.convert_to_tensor(__lowerCamelCase					)}					)

      return pt_input, tf_input



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
      if version.parse(huggingface_hub.__version__					) < version.parse("0.9.0"					):
         raise ImportError(
             "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
             " installation."					)
      else:
         from huggingface_hub import Repository, create_commit
         from huggingface_hub._commit_api import CommitOperationAdd

      # Fetch remote data
      _SCREAMING_SNAKE_CASE =   Repository(local_dir=self._local_dir						,   clone_from=self._model_name					)

      # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
      _SCREAMING_SNAKE_CASE =   AutoConfig.from_pretrained(self._local_dir					)
      _SCREAMING_SNAKE_CASE =   config.architectures
      if self._override_model_class is not None:
         if self._override_model_class.startswith("TF"					):
            _SCREAMING_SNAKE_CASE =   [self._override_model_class[2:]]
         else:
            _SCREAMING_SNAKE_CASE =   [self._override_model_class]
         try:
            _SCREAMING_SNAKE_CASE =   getattr(import_module("transformers"					)						,   architectures[0]					)
         except AttributeError:
            raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""					)
         try:
            _SCREAMING_SNAKE_CASE =   getattr(import_module("transformers"					)						,   "TF" + architectures[0]					)
         except AttributeError:
            raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers."""					)
      elif architectures is None:  # No architecture defined -- use auto classes
         _SCREAMING_SNAKE_CASE =   getattr(import_module("transformers"					)						,   "AutoModel"					)
         _SCREAMING_SNAKE_CASE =   getattr(import_module("transformers"					)						,   "TFAutoModel"					)
         self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"					)
      else:  # Architecture defined -- use it
         if len(__lowerCamelCase					) > 1:
            raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""					)
         self._logger.warning(F"""Detected architecture: {architectures[0]}"""					)
         _SCREAMING_SNAKE_CASE =   getattr(import_module("transformers"					)						,   architectures[0]					)
         try:
            _SCREAMING_SNAKE_CASE =   getattr(import_module("transformers"					)						,   "TF" + architectures[0]					)
         except AttributeError:
            raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""					)

        # Check the TF dummy inputs to see what keys we need in the forward pass
      _SCREAMING_SNAKE_CASE =   tf_class.from_config(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   tf_from_pt_model.dummy_inputs

      del tf_from_pt_model  # Try to keep only one model in memory at a time

      # Load the model and get some basic inputs
      _SCREAMING_SNAKE_CASE =   pt_class.from_pretrained(self._local_dir					)
      pt_model.eval()

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.get_inputs(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase					)

      with torch.no_grad():
         _SCREAMING_SNAKE_CASE =   pt_model(**__lowerCamelCase						,   output_hidden_states=__lowerCamelCase					)
      del pt_model  # will no longer be used, and may have a large memory footprint

      _SCREAMING_SNAKE_CASE =   tf_class.from_pretrained(self._local_dir						,   from_pt=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   tf_from_pt_model(**__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   training=__lowerCamelCase					)

      # Confirms that cross loading PT weights into TF worked.
      _SCREAMING_SNAKE_CASE =   self.find_pt_tf_differences(__lowerCamelCase						,   __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   {k: v for k, v in crossload_differences.items() if "hidden" not in k}
      _SCREAMING_SNAKE_CASE =   {k: v for k, v in crossload_differences.items() if "hidden" in k}
      if len(__lowerCamelCase					) == 0 and architectures is not None:
         raise ValueError(
             F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
             " output was found. All outputs start with 'hidden'"					)
      _SCREAMING_SNAKE_CASE =   max(output_differences.values()					) if output_differences else 0.0
      _SCREAMING_SNAKE_CASE =   max(hidden_differences.values()					)
      if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
         raise ValueError(
             "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
             + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]					)
             + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]					)					)

      # Save the weights in a TF format (if needed) and confirms that the results are still good
      _SCREAMING_SNAKE_CASE =   os.path.join(self._local_dir						,   __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   os.path.join(self._local_dir						,   __lowerCamelCase					)
      if (not os.path.exists(__lowerCamelCase					) and not os.path.exists(__lowerCamelCase					)) or self._new_weights:
         tf_from_pt_model.save_pretrained(self._local_dir					)
      del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

      _SCREAMING_SNAKE_CASE =   tf_class.from_pretrained(self._local_dir					)
      _SCREAMING_SNAKE_CASE =   tf_model(**__lowerCamelCase						,   output_hidden_states=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.find_pt_tf_differences(__lowerCamelCase						,   __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   {k: v for k, v in conversion_differences.items() if "hidden" not in k}
      _SCREAMING_SNAKE_CASE =   {k: v for k, v in conversion_differences.items() if "hidden" in k}
      if len(__lowerCamelCase					) == 0 and architectures is not None:
         raise ValueError(
             F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
             " output was found. All outputs start with 'hidden'"					)
      _SCREAMING_SNAKE_CASE =   max(output_differences.values()					) if output_differences else 0.0
      _SCREAMING_SNAKE_CASE =   max(hidden_differences.values()					)
      if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
         raise ValueError(
             "The converted TensorFlow model has different outputs, something went wrong!\n"
             + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]					)
             + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]					)					)

      _SCREAMING_SNAKE_CASE =   "Update TF weights" if self._new_weights else "Add TF weights"
      if self._push:
         repo.git_add(auto_lfs_track=__lowerCamelCase					)
         repo.git_commit(__lowerCamelCase					)
         repo.git_push(blocking=__lowerCamelCase					)  # this prints a progress bar with the upload
         self._logger.warning(F"""TF weights pushed into {self._model_name}"""					)
      elif not self._no_pr:
         self._logger.warning("Uploading the weights into a new PR..."					)
         _SCREAMING_SNAKE_CASE =   (
             "Model converted by the [`transformers`' `pt_to_tf`"
             " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
             "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
             F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
             F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
             F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
             F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
         )
         if self._max_error > MAX_ERROR:
            commit_descrition += (
                F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
            )
         if self._extra_commit_description:
            commit_descrition += "\n\n" + self._extra_commit_description

         # sharded model -> adds all related files (index and .h5 shards)
         if os.path.exists(__lowerCamelCase					):
            _SCREAMING_SNAKE_CASE =   [
                CommitOperationAdd(path_in_repo=__lowerCamelCase						,   path_or_fileobj=__lowerCamelCase					)
            ]
            for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"					):
               operations += [
                   CommitOperationAdd(path_in_repo=os.path.basename(__lowerCamelCase					)						,   path_or_fileobj=__lowerCamelCase					)
               ]
         else:
            _SCREAMING_SNAKE_CASE =   [CommitOperationAdd(path_in_repo=__lowerCamelCase						,   path_or_fileobj=__lowerCamelCase					)]

         _SCREAMING_SNAKE_CASE =   create_commit(
             repo_id=self._model_name						,   operations=__lowerCamelCase						,   commit_message=__lowerCamelCase						,   commit_description=__lowerCamelCase						,   repo_type="model"						,   create_pr=__lowerCamelCase						,   ).pr_url
         self._logger.warning(F"""PR open in {hub_pr_url}"""					)







'''simple docstring'''



lowerCamelCase_	 =		[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      self.inbound.append(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      self.outbound.append(__lowerCamelCase					)



   def __repr__(					self      :			List[str]					):

      """simple docstring"""
      return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]=3 ,  __A       :				Optional[int]=0.8_5						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   1

   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   len(node.outbound						)

   for i in range(__A						):
      print(f"""======= Iteration {i + 1} ======="""						)
      for _, node in enumerate(__A						):
         _SCREAMING_SNAKE_CASE =   (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound						)
      print(__A						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   list(input("Enter Names of the Nodes: "						).split()						)

   _SCREAMING_SNAKE_CASE =   [Node(__A						) for name in names]

   for ri, row in enumerate(__A						):
      for ci, col in enumerate(__A						):
         if col == 1:
            nodes[ci].add_inbound(names[ri]						)
            nodes[ri].add_outbound(names[ci]						)

   print("======= Nodes ======="						)
   for node in nodes:
      print(__A						)

   page_rank(__A						)


if __name__ == "__main__":
 main()

