


import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
   import tensorflow as tf

   tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
   import torch

if is_datasets_available():
   from datasets import load_dataset


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

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Dict:




       '''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 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     @staticmethod
     def   lowerCAmelCase_   ( UpperCamelCase       :       ArgumentParser		):





            """simple docstring"""



            _lowercase					:				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=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   lowerCAmelCase_   ( UpperCamelCase       :       int	,      UpperCamelCase       :       str		):





            """simple docstring"""



            _lowercase					:				Dict           =			set(pt_outputs.keys()		)
            _lowercase					:				str           =			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       :       Optional[int]	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       str=""		):
                   # 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					:				str           =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()		)		)
                          _lowercase					:				Dict           =			tensor_difference
                   else:
                          _lowercase					:				Tuple           =			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					:				List[str]           =			root_name + pt_item
                                        _lowercase					:				Dict           =			tf_out[pt_item]
                                        _lowercase					:				Tuple           =			pt_out[pt_item]
                                 else:
                                        _lowercase					:				Dict           =			root_name + F'[{i}]'
                                        _lowercase					:				List[Any]           =			tf_out[i]
                                 _lowercase					:				Any           =			_find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)

                   return differences

            return _find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase	,      {}		)




     def __init__( self       :       Tuple	,      UpperCamelCase       :       str	,      UpperCamelCase       :       str	,      UpperCamelCase       :       float	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       bool	,      UpperCamelCase       :       str	,      UpperCamelCase       :       str	,      *UpperCamelCase       :       Dict	,      ):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			logging.get_logger('''transformers-cli/pt_to_tf'''		)
            _lowercase					:				Optional[Any]           =			model_name
            _lowercase					:				int           =			local_dir if local_dir else os.path.join('''/tmp'''	,      UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			max_error
            _lowercase					:				List[Any]           =			new_weights
            _lowercase					:				Union[str, Any]           =			no_pr
            _lowercase					:				Tuple           =			push
            _lowercase					:				Tuple           =			extra_commit_description
            _lowercase					:				Optional[int]           =			override_model_class




     def   lowerCAmelCase_   ( self       :       Tuple	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       List[Any]	,      UpperCamelCase       :       Union[str, Any]		):





            """simple docstring"""



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

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

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





            """simple docstring"""



            if version.parse(huggingface_hub.__version__		) < version.parse('''0.9.0'''		):
                   raise ImportError(
                       '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
                       ''' installation.'''		)
            else:
                   from huggingface_hub import Repository, create_commit
                   from huggingface_hub._commit_api import CommitOperationAdd

            # Fetch remote data
            _lowercase					:				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
            _lowercase					:				List[str]           =			AutoConfig.from_pretrained(self._local_dir		)
            _lowercase					:				Tuple           =			config.architectures
            if self._override_model_class is not None:
                   if self._override_model_class.startswith('''TF'''		):
                          _lowercase					:				int           =			[self._override_model_class[2:]]
                   else:
                          _lowercase					:				Union[str, Any]           =			[self._override_model_class]
                   try:
                          _lowercase					:				str           =			getattr(import_module('''transformers'''		)	,      architectures[0]		)
                   except AttributeError:
                          raise ValueError(F'Model class {self._override_model_class} not found in transformers.'		)
                   try:
                          _lowercase					:				str           =			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					:				Tuple           =			getattr(import_module('''transformers'''		)	,      '''AutoModel'''		)
                   _lowercase					:				Any           =			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					:				Optional[int]           =			getattr(import_module('''transformers'''		)	,      architectures[0]		)
                   try:
                          _lowercase					:				Dict           =			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					:				List[str]           =			tf_class.from_config(UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			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					:				List[Any]           =			pt_class.from_pretrained(self._local_dir		)
            pt_model.eval()

            _lowercase       ,	_lowercase					:				Union[str, Any]           =			self.get_inputs(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)

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

            _lowercase					:				Dict           =			tf_class.from_pretrained(self._local_dir	,      from_pt=UpperCamelCase		)
            _lowercase					:				List[Any]           =			tf_from_pt_model(**UpperCamelCase	,      output_hidden_states=UpperCamelCase	,      training=UpperCamelCase		)

            # Confirms that cross loading PT weights into TF worked.
            _lowercase					:				Optional[int]           =			self.find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Any           =			{k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
            _lowercase					:				List[str]           =			{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					:				Union[str, Any]           =			max(output_differences.values()		) if output_differences else 0.0
            _lowercase					:				Tuple           =			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					:				Any           =			os.path.join(self._local_dir	,      UpperCamelCase		)
            _lowercase					:				int           =			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					:				int           =			tf_class.from_pretrained(self._local_dir		)
            _lowercase					:				Optional[int]           =			tf_model(**UpperCamelCase	,      output_hidden_states=UpperCamelCase		)

            _lowercase					:				int           =			self.find_pt_tf_differences(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
            _lowercase					:				int           =			{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					:				Optional[int]           =			max(output_differences.values()		) if output_differences else 0.0
            _lowercase					:				int           =			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					:				Tuple           =			'''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					:				Any           =			(
                       '''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					:				List[str]           =			[
                              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					:				Any           =			[CommitOperationAdd(path_in_repo=UpperCamelCase	,      path_or_fileobj=UpperCamelCase		)]

                   _lowercase					:				List[str]           =			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}'		)



import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCamelCase__				       =			get_logger(__name__)

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:




       '''simple docstring'''






       if text.startswith(UpperCAmelCase_			):
              return text[len(UpperCAmelCase_			) :]
       return text  # or whatever

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:




       '''simple docstring'''






       return {remove_prefix(UpperCAmelCase_    ,     '''model.'''			): v for k, v in sd.items()}

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:




       '''simple docstring'''






       _lowercase					:				Optional[int]           =			{}
       for k in state_dicts[0].keys():
              _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
              _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
              assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
              _lowercase					:				Union[str, Any]           =			new_t
       return new_sd

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:




       '''simple docstring'''






       _lowercase					:				List[str]           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_			)
       if os.path.isfile(UpperCAmelCase_			):
              _lowercase					:				Optional[Any]           =			[pl_ckpt_path]
       else:
              assert os.path.isdir(UpperCAmelCase_			)
              _lowercase					:				List[str]           =			list(Path(UpperCAmelCase_			).glob('''*.ckpt'''			)			)
              assert ckpt_files, F'could not find any ckpt files inside the {pl_ckpt_path} directory'

       if len(UpperCAmelCase_			) > 1:
              logger.info(F'averaging the weights of {ckpt_files}'			)

       _lowercase					:				List[Any]           =			[sanitize(torch.load(UpperCAmelCase_    ,     map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
       _lowercase					:				Optional[Any]           =			average_state_dicts(UpperCAmelCase_			)

       _lowercase       ,	_lowercase					:				str           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
       assert not missing, F'missing keys: {missing}'
       hf_model.save_pretrained(UpperCAmelCase_			)
       try:
              _lowercase					:				Union[str, Any]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_			)
              tok.save_pretrained(UpperCAmelCase_			)
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
   fire.Fire(convert_pl_to_hf)