





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


_UpperCAmelCase										=					5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		List[Any]:
       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 				_UpperCamelCase       (						lowerCAmelCase_  ):

       @staticmethod
       def lowercase			(		_SCREAMING_SNAKE_CASE:			ArgumentParser		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE       , required=_SCREAMING_SNAKE_CASE       , help="The model name, including owner/organization, as seen on the hub."       , )
              train_parser.add_argument(
                  "--local-dir"       , type=_SCREAMING_SNAKE_CASE       , default=""       , help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"       , )
              train_parser.add_argument(
                  "--max-error"       , type=_SCREAMING_SNAKE_CASE       , default=_SCREAMING_SNAKE_CASE       , 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=_SCREAMING_SNAKE_CASE       , 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=_SCREAMING_SNAKE_CASE       , default=_SCREAMING_SNAKE_CASE       , 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=_SCREAMING_SNAKE_CASE		)

       @staticmethod
       def lowercase			(		_SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	set(pt_outputs.keys()		)
              UpperCamelCase_ 							=	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(_SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Dict=""		):
                     # 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(_SCREAMING_SNAKE_CASE       , torch.Tensor		):
                            UpperCamelCase_ 							=	np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
                            UpperCamelCase_ 							=	tensor_difference
                     else:
                            UpperCamelCase_ 							=	attr_name
                            for i, pt_item in enumerate(_SCREAMING_SNAKE_CASE		):
                                   # If it is a named attribute, we keep the name. Otherwise, just its index.
                                   if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                                          UpperCamelCase_ 							=	root_name + pt_item
                                          UpperCamelCase_ 							=	tf_out[pt_item]
                                          UpperCamelCase_ 							=	pt_out[pt_item]
                                   else:
                                          UpperCamelCase_ 							=	root_name + f'''[{i}]'''
                                          UpperCamelCase_ 							=	tf_out[i]
                                   UpperCamelCase_ 							=	_find_pt_tf_differences(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

                     return differences

              return _find_pt_tf_differences(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , {}		)

       def __init__(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			float       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			str       , *_SCREAMING_SNAKE_CASE:			Dict       , )							->			str:




              """simple docstring"""


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

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			int		)							->			Dict:




              """simple docstring"""


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

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

              UpperCamelCase_ 							=	set(inspect.signature(pt_model.forward		).parameters.keys()		)
              UpperCamelCase_ 							=	{}
              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:
                     UpperCamelCase_ 							=	load_dataset("cifar10"       , "plain_text"       , split="test"		)[:2]["img"]
                     processor_inputs.update({"images": sample_images}		)
              if "input_features" in model_forward_signature:
                     UpperCamelCase_ 							=	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:
                            UpperCamelCase_ 							=	feature_extractor_signature["padding"].default
                            if default_strategy is not False and default_strategy is not None:
                                   UpperCamelCase_ 							=	default_strategy
                            else:
                                   UpperCamelCase_ 							=	True
                     else:
                            UpperCamelCase_ 							=	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}		)
              UpperCamelCase_ 							=	processor(**_SCREAMING_SNAKE_CASE       , return_tensors="pt"		)
              UpperCamelCase_ 							=	processor(**_SCREAMING_SNAKE_CASE       , return_tensors="tf"		)

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

              return pt_input, tf_input



       def lowercase			(		self:			Tuple		)							->			Optional[Any]:




              """simple docstring"""


              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
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	AutoConfig.from_pretrained(self._local_dir		)
              UpperCamelCase_ 							=	config.architectures
              if self._override_model_class is not None:
                     if self._override_model_class.startswith("TF"		):
                            UpperCamelCase_ 							=	[self._override_model_class[2:]]
                     else:
                            UpperCamelCase_ 							=	[self._override_model_class]
                     try:
                            UpperCamelCase_ 							=	getattr(import_module("transformers"		)       , architectures[0]		)
                     except AttributeError:
                            raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''		)
                     try:
                            UpperCamelCase_ 							=	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
                     UpperCamelCase_ 							=	getattr(import_module("transformers"		)       , "AutoModel"		)
                     UpperCamelCase_ 							=	getattr(import_module("transformers"		)       , "TFAutoModel"		)
                     self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"		)
              else:  # Architecture defined -- use it
                     if len(_SCREAMING_SNAKE_CASE		) > 1:
                            raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''		)
                     self._logger.warning(f'''Detected architecture: {architectures[0]}'''		)
                     UpperCamelCase_ 							=	getattr(import_module("transformers"		)       , architectures[0]		)
                     try:
                            UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	tf_class.from_config(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	pt_class.from_pretrained(self._local_dir		)
              pt_model.eval()

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

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

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

              # Confirms that cross loading PT weights into TF worked.
              UpperCamelCase_ 							=	self.find_pt_tf_differences(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{k: v for k, v in crossload_differences.items() if "hidden" not in k}
              UpperCamelCase_ 							=	{k: v for k, v in crossload_differences.items() if "hidden" in k}
              if len(_SCREAMING_SNAKE_CASE		) == 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'"		)
              UpperCamelCase_ 							=	max(output_differences.values()		) if output_differences else 0.0
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	os.path.join(self._local_dir       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	os.path.join(self._local_dir       , _SCREAMING_SNAKE_CASE		)
              if (not os.path.exists(_SCREAMING_SNAKE_CASE		) and not os.path.exists(_SCREAMING_SNAKE_CASE		)) 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

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

              UpperCamelCase_ 							=	self.find_pt_tf_differences(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{k: v for k, v in conversion_differences.items() if "hidden" not in k}
              UpperCamelCase_ 							=	{k: v for k, v in conversion_differences.items() if "hidden" in k}
              if len(_SCREAMING_SNAKE_CASE		) == 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'"		)
              UpperCamelCase_ 							=	max(output_differences.values()		) if output_differences else 0.0
              UpperCamelCase_ 							=	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]		)		)

              UpperCamelCase_ 							=	"Update TF weights" if self._new_weights else "Add TF weights"
              if self._push:
                     repo.git_add(auto_lfs_track=_SCREAMING_SNAKE_CASE		)
                     repo.git_commit(_SCREAMING_SNAKE_CASE		)
                     repo.git_push(blocking=_SCREAMING_SNAKE_CASE		)  # 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..."		)
                     UpperCamelCase_ 							=	(
                         "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(_SCREAMING_SNAKE_CASE		):
                            UpperCamelCase_ 							=	[
                                CommitOperationAdd(path_in_repo=_SCREAMING_SNAKE_CASE       , path_or_fileobj=_SCREAMING_SNAKE_CASE		)
                            ]
                            for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"		):
                                   operations += [
                                       CommitOperationAdd(path_in_repo=os.path.basename(_SCREAMING_SNAKE_CASE		)       , path_or_fileobj=_SCREAMING_SNAKE_CASE		)
                                   ]
                     else:
                            UpperCamelCase_ 							=	[CommitOperationAdd(path_in_repo=_SCREAMING_SNAKE_CASE       , path_or_fileobj=_SCREAMING_SNAKE_CASE		)]

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





from __future__ import annotations
def 							UpperCAmelCase_   (				__snake_case		)	->     list[int]:







 """simple docstring"""


 if not list_of_ints:
  return []

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     list[int]:







 """simple docstring"""


 if bit_position == 0 or len(__snake_case		) in [0, 1]:
  return list_of_ints

 _lowercase				 =[]
 _lowercase				 =[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(__snake_case		)
  else:
   # number has a zero at bit bit_position
   zeros.append(__snake_case		)

    # recursively split both lists further
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)

 # recombine lists
 _lowercase				 =zeros
 res.extend(__snake_case		)

 return res
def 							UpperCAmelCase_   (				__snake_case		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =len(__snake_case		)
 if not list_of_ints or length == 1:
  return

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		0	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 _lowercase				 =begin_index
 _lowercase				 =end_index - 1
 while i <= j:
  _lowercase				 =False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   _lowercase				 =True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   _lowercase				 =True

  if changed:
   continue

  _lowercase ,       _lowercase				 =list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

