
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


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




def     a						(							snake_case__: List[str] ):


				'''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 			lowercase__(    _lowerCamelCase							):



	"""simple docstring"""




	@staticmethod
	def 						_lowercase  (	SCREAMING_SNAKE_CASE_  :      Tuple	)	->					int:
					lowercase_					  = 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 						_lowercase  (	SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	)	->					Optional[Any]:
					lowercase_					  = set(pt_outputs.keys()	)
					lowercase_					  = 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_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      Optional[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(__UpperCamelCase	,							torch.Tensor	):
													lowercase_					  = np.max(np.abs(pt_out.numpy() - tf_out.numpy()	)	)
													lowercase_					  = tensor_difference
									else:
													lowercase_					  = 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	):
																					lowercase_					  = root_name + pt_item
																					lowercase_					  = tf_out[pt_item]
																					lowercase_					  = pt_out[pt_item]
																	else:
																					lowercase_					  = root_name + f'''[{i}]'''
																					lowercase_					  = tf_out[i]
																	lowercase_					  = _find_pt_tf_differences(__UpperCamelCase	,							__UpperCamelCase	,							__UpperCamelCase	,							__UpperCamelCase	)

									return differences

					return _find_pt_tf_differences(__UpperCamelCase	,							__UpperCamelCase	,							{}	)
	def __init__(	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							*SCREAMING_SNAKE_CASE_  :      Tuple	,							)	->					List[Any]:
					lowercase_					  = logging.get_logger('''transformers-cli/pt_to_tf'''	)
					lowercase_					  = model_name
					lowercase_					  = local_dir if local_dir else os.path.join('''/tmp'''	,							__UpperCamelCase	)
					lowercase_					  = max_error
					lowercase_					  = new_weights
					lowercase_					  = no_pr
					lowercase_					  = push
					lowercase_					  = extra_commit_description
					lowercase_					  = override_model_class
	def 						_lowercase  (	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      List[Any]	)	->					Optional[Any]:







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

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

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







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

					lowercase_					  = self.get_inputs(__UpperCamelCase	,							__UpperCamelCase	,							__UpperCamelCase	)

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

					lowercase_					  = tf_class.from_pretrained(self._local_dir	,							from_pt=__UpperCamelCase	)
					lowercase_					  = tf_from_pt_model(**__UpperCamelCase	,							output_hidden_states=__UpperCamelCase	,							training=__UpperCamelCase	)

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

					lowercase_					  = tf_class.from_pretrained(self._local_dir	)
					lowercase_					  = tf_model(**__UpperCamelCase	,							output_hidden_states=__UpperCamelCase	)

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

					lowercase_					  = '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...'''	)
									lowercase_					  = (
									    '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	):
													lowercase_					  = [
													    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:
													lowercase_					  = [CommitOperationAdd(path_in_repo=__UpperCamelCase	,							path_or_fileobj=__UpperCamelCase	)]

									lowercase_					  = 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}'''	)






from __future__ import annotations

__a        = 'Alexander Joslin'




def     a						(							snake_case__: int ):


				'''simple docstring'''

				if number <= 0:
								lowercase_					  = F'''n must be greater than 0. Got n = {number}'''
								raise ValueError(snake_case__ )

				lowercase_					  = [number + 1] * (number + 1)

				# starting position
				lowercase_					  = 0
				for i in range(1						,       snake_case__ ):
								lowercase_					  = min(table[i + 1]						,       table[i] + 1 )
								# check if out of bounds
								if i * 2 <= number:
												lowercase_					  = min(table[i * 2]						,       table[i] + 1 )
								# check if out of bounds
								if i * 3 <= number:
												lowercase_					  = min(table[i * 3]						,       table[i] + 1 )
				return table[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




