


"""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


__snake_case												=							5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def    A_ ( _lowerCAmelCase  :       Namespace							):






     """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	(       lowercase_					):

     '''simple docstring'''


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase				)							->				List[Any]:
          _a     						=					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=__UpperCAmelCase     ,					required=__UpperCAmelCase     ,					help='''The model name, including owner/organization, as seen on the hub.'''     ,					)
          train_parser.add_argument(
              '''--local-dir'''     ,					type=__UpperCAmelCase     ,					default=''''''     ,					help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''     ,					)
          train_parser.add_argument(
              '''--max-error'''     ,					type=__UpperCAmelCase     ,					default=__UpperCAmelCase     ,					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=__UpperCAmelCase     ,					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=__UpperCAmelCase     ,					default=__UpperCAmelCase     ,					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=__UpperCAmelCase				)


     @staticmethod
     def 				_UpperCAmelCase   (   __UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[Any]:
          _a     						=					set(pt_outputs.keys()				)
          _a     						=					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(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=""				):
               # 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(__UpperCAmelCase     ,					torch.Tensor				):
                    _a     						=					np.max(np.abs(pt_out.numpy() - tf_out.numpy()				)				)
                    _a     						=					tensor_difference
               else:
                    _a     						=					attr_name
                    for i, pt_item in enumerate(__UpperCAmelCase				):
                         # If it is a named attribute, we keep the name. Otherwise, just its index.
                         if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
                              _a     						=					root_name + pt_item
                              _a     						=					tf_out[pt_item]
                              _a     						=					pt_out[pt_item]
                         else:
                              _a     						=					root_name + F'[{i}]'
                              _a     						=					tf_out[i]
                         _a     						=					_find_pt_tf_differences(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

               return differences

          return _find_pt_tf_differences(__UpperCAmelCase     ,					__UpperCAmelCase     ,					{}				)


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					*__UpperCAmelCase     ,					)							->				Dict:
          _a     						=					logging.get_logger('''transformers-cli/pt_to_tf'''				)
          _a     						=					model_name
          _a     						=					local_dir if local_dir else os.path.join('''/tmp'''     ,					__UpperCAmelCase				)
          _a     						=					max_error
          _a     						=					new_weights
          _a     						=					no_pr
          _a     						=					push
          _a     						=					extra_commit_description
          _a     						=					override_model_class


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[Any]:

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

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

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

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

          return pt_input, tf_input





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

          _a						,   _a     						=					self.get_inputs(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

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

          _a     						=					tf_class.from_pretrained(self._local_dir     ,					from_pt=__UpperCAmelCase				)
          _a     						=					tf_from_pt_model(**__UpperCAmelCase     ,					output_hidden_states=__UpperCAmelCase     ,					training=__UpperCAmelCase				)

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

          _a     						=					tf_class.from_pretrained(self._local_dir				)
          _a     						=					tf_model(**__UpperCAmelCase     ,					output_hidden_states=__UpperCAmelCase				)

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

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

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





"""simple docstring"""

from __future__ import annotations




def    snake_case__						(							lowerCAmelCase :		list[int]		):
					__snake_case        =						[0] * len(lowerCAmelCase		)
					for i in range(1					,       len(lowerCAmelCase		)		):
										__snake_case        =						duration_times[i - 1] + waiting_times[i - 1]
					return waiting_times




def    snake_case__						(							lowerCAmelCase :		list[int]					,       lowerCAmelCase :		list[int]		):
					return [
					    duration_time + waiting_times[i]
					    for i, duration_time in enumerate(lowerCAmelCase		)
					]




def    snake_case__						(							lowerCAmelCase :		list[int]		):
					return sum(lowerCAmelCase		) / len(lowerCAmelCase		)




def    snake_case__						(							lowerCAmelCase :		list[int]		):
					return sum(lowerCAmelCase		) / len(lowerCAmelCase		)


if __name__ == "__main__":
		# process id's
		UpperCamelCase__			:     str			       =				[1, 2, 3]

		# ensure that we actually have processes
		if len(processes) == 0:
				print("Zero amount of processes")
				raise SystemExit(0)

		# duration time of all processes
		UpperCamelCase__			:     Any			       =				[19, 8, 9]

		# ensure we can match each id to a duration time
		if len(duration_times) != len(processes):
				print("Unable to match all id's with their duration time")
				raise SystemExit(0)

		# get the waiting times and the turnaround times
		UpperCamelCase__			:     Optional[int]			       =				calculate_waiting_times(duration_times)
		UpperCamelCase__			:     Any			       =				calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		UpperCamelCase__			:     Optional[Any]			       =				calculate_average_waiting_time(waiting_times)
		UpperCamelCase__			:     Dict			       =				calculate_average_turnaround_time(turnaround_times)

		# print all the results
		print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
		for i, process in enumerate(processes):
				print(
				    f'{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'
				    f'{turnaround_times[i]}'
				)
		print(f'Average waiting time = {average_waiting_time}')
		print(f'Average turn around time = {average_turnaround_time}')


