




'''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


UpperCAmelCase_    :					int					      =						5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def      _UpperCamelCase	(_lowerCamelCase							:       Union[str, Any]		)->				Optional[Any]:




     '''simple docstring'''





     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       lowerCAmelCase	(	_lowerCAmelCase):


       @staticmethod
       def  lowerCAmelCase (				__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __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 (				__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




            __snake_case         =					set(pt_outputs.keys()					)
            __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(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=""					):
                 # 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					):
                      __snake_case         =					np.max(np.abs(pt_out.numpy() - tf_out.numpy()					)					)
                      __snake_case         =					tensor_difference
                 else:
                      __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					):
                                __snake_case         =					root_name + pt_item
                                __snake_case         =					tf_out[pt_item]
                                __snake_case         =					pt_out[pt_item]
                           else:
                                __snake_case         =					root_name + F'''[{i}]'''
                                __snake_case         =					tf_out[i]
                           __snake_case         =					_find_pt_tf_differences(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase					)

                 return differences

            return _find_pt_tf_differences(_lowerCamelCase      ,					_lowerCamelCase      ,					{}					)


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					*__SCREAMING_SNAKE_CASE      ,					) ->					Dict:



            '''simple docstring'''




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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					str:



            '''simple docstring'''




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

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

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




            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
            __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
            __snake_case         =					AutoConfig.from_pretrained(self._local_dir					)
            __snake_case         =					config.architectures
            if self._override_model_class is not None:
                 if self._override_model_class.startswith('''TF'''					):
                      __snake_case         =					[self._override_model_class[2:]]
                 else:
                      __snake_case         =					[self._override_model_class]
                 try:
                      __snake_case         =					getattr(import_module('''transformers'''					)      ,					architectures[0]					)
                 except AttributeError:
                      raise ValueError(F'''Model class {self._override_model_class} not found in transformers.'''					)
                 try:
                      __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
                 __snake_case         =					getattr(import_module('''transformers'''					)      ,					'''AutoModel'''					)
                 __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]}'''					)
                 __snake_case         =					getattr(import_module('''transformers'''					)      ,					architectures[0]					)
                 try:
                      __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
            __snake_case         =					tf_class.from_config(_lowerCamelCase					)
            __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
            __snake_case         =					pt_class.from_pretrained(self._local_dir					)
            pt_model.eval()

            __snake_case   ,      __snake_case         =					self.get_inputs(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase					)

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

            __snake_case         =					tf_class.from_pretrained(self._local_dir      ,					from_pt=_lowerCamelCase					)
            __snake_case         =					tf_from_pt_model(**_lowerCamelCase      ,					output_hidden_states=_lowerCamelCase      ,					training=_lowerCamelCase					)

            # Confirms that cross loading PT weights into TF worked.
            __snake_case         =					self.find_pt_tf_differences(_lowerCamelCase      ,					_lowerCamelCase					)
            __snake_case         =					{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
            __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\''''					)
            __snake_case         =					max(output_differences.values()					) if output_differences else 0.0
            __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
            __snake_case         =					os.path.join(self._local_dir      ,					_lowerCamelCase					)
            __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

            __snake_case         =					tf_class.from_pretrained(self._local_dir					)
            __snake_case         =					tf_model(**_lowerCamelCase      ,					output_hidden_states=_lowerCamelCase					)

            __snake_case         =					self.find_pt_tf_differences(_lowerCamelCase      ,					_lowerCamelCase					)
            __snake_case         =					{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
            __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\''''					)
            __snake_case         =					max(output_differences.values()					) if output_differences else 0.0
            __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]					)					)

            __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...'''					)
                 __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					):
                      __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:
                      __snake_case         =					[CommitOperationAdd(path_in_repo=_lowerCamelCase      ,					path_or_fileobj=_lowerCamelCase					)]

                 __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'''



def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     for index, item in enumerate(_lowerCamelCase		):
          if item == target:
               return index
     return -1







def      _UpperCamelCase	(_lowerCamelCase							:       list				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int				,		_lowerCamelCase							:       int		)->				int:




     '''simple docstring'''





     if not (0 <= high < len(_lowerCamelCase		) and 0 <= low < len(_lowerCamelCase		)):
          raise Exception('''Invalid upper or lower bound!'''		)
     if high < low:
          return -1
     if sequence[low] == target:
          return low
     if sequence[high] == target:
          return high
     return rec_linear_search(_lowerCamelCase				,		low + 1				,		high - 1				,		_lowerCamelCase		)


if __name__ == "__main__":
     UpperCAmelCase_    :					List[Any]					      =						input('''Enter numbers separated by comma:\n''').strip()
     UpperCAmelCase_    :					Optional[int]					      =						[int(item.strip()) for item in user_input.split(''',''')]

     UpperCAmelCase_    :					List[str]					      =						int(input('''Enter a single number to be found in the list:\n''').strip())
     UpperCAmelCase_    :					Optional[int]					      =						linear_search(sequence, target)
     if result != -1:
          print(F"""linear_search({sequence}, {target}) = {result}""")
     else:
          print(F"""{target} was not found in {sequence}""")






