
'''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


__lowercase							:				List[Any]					=       5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def lowercase_		(				_lowercase			)       ->							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 		__lowercase			(					_lowercase      ):

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

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

													return differences

								return _find_pt_tf_differences(A     ,				A     ,				{} )

			def __init__(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				*A     ,				):
								lowerCamelCase_   : int       =						logging.get_logger('''transformers-cli/pt_to_tf''' )
								lowerCamelCase_   : Optional[int]       =						model_name
								lowerCamelCase_   : int       =						local_dir if local_dir else os.path.join('''/tmp'''     ,				A )
								lowerCamelCase_   : str       =						max_error
								lowerCamelCase_   : str       =						new_weights
								lowerCamelCase_   : Dict       =						no_pr
								lowerCamelCase_   : str       =						push
								lowerCamelCase_   : Optional[Any]       =						extra_commit_description
								lowerCamelCase_   : int       =						override_model_class

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):

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

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

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

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

								return pt_input, tf_input






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

								lowerCamelCase_, lowerCamelCase_   : Dict       =						self.get_inputs(A     ,				A     ,				A )

								with torch.no_grad():
													lowerCamelCase_   : Optional[int]       =						pt_model(**A     ,				output_hidden_states=A )
								del pt_model  # will no longer be used, and may have a large memory footprint

								lowerCamelCase_   : List[Any]       =						tf_class.from_pretrained(self._local_dir     ,				from_pt=A )
								lowerCamelCase_   : Optional[int]       =						tf_from_pt_model(**A     ,				output_hidden_states=A     ,				training=A )

								# Confirms that cross loading PT weights into TF worked.
								lowerCamelCase_   : str       =						self.find_pt_tf_differences(A     ,				A )
								lowerCamelCase_   : List[str]       =						{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
								lowerCamelCase_   : List[str]       =						{k: v for k, v in crossload_differences.items() if '''hidden''' in k}
								if len(A ) == 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\'''' )
								lowerCamelCase_   : Optional[Any]       =						max(output_differences.values() ) if output_differences else 0.0
								lowerCamelCase_   : Optional[int]       =						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
								lowerCamelCase_   : Any       =						os.path.join(self._local_dir     ,				A )
								lowerCamelCase_   : Any       =						os.path.join(self._local_dir     ,				A )
								if (not os.path.exists(A ) and not os.path.exists(A )) 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

								lowerCamelCase_   : Tuple       =						tf_class.from_pretrained(self._local_dir )
								lowerCamelCase_   : Tuple       =						tf_model(**A     ,				output_hidden_states=A )

								lowerCamelCase_   : Tuple       =						self.find_pt_tf_differences(A     ,				A )
								lowerCamelCase_   : List[str]       =						{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
								lowerCamelCase_   : int       =						{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
								if len(A ) == 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\'''' )
								lowerCamelCase_   : Optional[int]       =						max(output_differences.values() ) if output_differences else 0.0
								lowerCamelCase_   : List[Any]       =						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] ) )

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

													lowerCamelCase_   : str       =						create_commit(
													    repo_id=self._model_name     ,				operations=A     ,				commit_message=A     ,				commit_description=A     ,				repo_type='''model'''     ,				create_pr=A     ,				).pr_url
													self._logger.warning(F"""PR open in {hub_pr_url}""" )




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowercase							:				List[str]					=       {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				int					=       [
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''SamImageProcessor''']


if TYPE_CHECKING:
					from .configuration_sam import (
					    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SamConfig,
					    SamMaskDecoderConfig,
					    SamPromptEncoderConfig,
					    SamVisionConfig,
					)
					from .processing_sam import SamProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_sam import SamImageProcessor

else:
					import sys

					__lowercase							:				List[str]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


