





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


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    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 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    @staticmethod
    def    lowercase_				(					A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= parser.add_parser(
            '''pt-to-tf'''      ,							help=(
                '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
                ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
            )      ,							)
        train_parser.add_argument(
            '''--model-name'''      ,							type=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    lowercase_				(					A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= set(pt_outputs.keys()    )
        SCREAMING_SNAKE_CASE__       							= set(tf_outputs.keys()    )
        if pt_out_attrs != tf_out_attrs:
            raise ValueError(
                f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
                f''' {tf_out_attrs})'''    )

        # 2. For each output attribute, computes the difference
        def _find_pt_tf_differences(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    ):
                SCREAMING_SNAKE_CASE__       							= np.max(np.abs(pt_out.numpy() - tf_out.numpy()    )    )
                SCREAMING_SNAKE_CASE__       							= tensor_difference
            else:
                SCREAMING_SNAKE_CASE__       							= 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_    ):
                        SCREAMING_SNAKE_CASE__       							= root_name + pt_item
                        SCREAMING_SNAKE_CASE__       							= tf_out[pt_item]
                        SCREAMING_SNAKE_CASE__       							= pt_out[pt_item]
                    else:
                        SCREAMING_SNAKE_CASE__       							= root_name + f'''[{i}]'''
                        SCREAMING_SNAKE_CASE__       							= tf_out[i]
                    SCREAMING_SNAKE_CASE__       							= _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_      ,							):





        '''simple docstring'''

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



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__       							= set(inspect.signature(pt_model.forward    ).parameters.keys()    )
        SCREAMING_SNAKE_CASE__       							= {}
        if "input_ids" in model_forward_signature:
            processor_inputs.update(
                {
                    '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
                    '''padding''': True,
                    '''truncation''': True,
                }    )
        if "pixel_values" in model_forward_signature:
            SCREAMING_SNAKE_CASE__       							= load_dataset('''cifar10'''      ,							'''plain_text'''      ,							split='''test'''    )[:2]['''img''']
            processor_inputs.update({'''images''': sample_images}    )
        if "input_features" in model_forward_signature:
            SCREAMING_SNAKE_CASE__       							= inspect.signature(processor.feature_extractor    ).parameters
            # Pad to the largest input length by default but take feature extractor default
            # padding value if it exists e.g. "max_length" and is not False or None
            if "padding" in feature_extractor_signature:
                SCREAMING_SNAKE_CASE__       							= feature_extractor_signature['''padding'''].default
                if default_strategy is not False and default_strategy is not None:
                    SCREAMING_SNAKE_CASE__       							= default_strategy
                else:
                    SCREAMING_SNAKE_CASE__       							= True
            else:
                SCREAMING_SNAKE_CASE__       							= True
            processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}    )
        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
            processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}    )
        SCREAMING_SNAKE_CASE__       							= processor(**A_      ,							return_tensors='''pt'''    )
        SCREAMING_SNAKE_CASE__       							= 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
        ):
            SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self    ):





        '''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
        SCREAMING_SNAKE_CASE__       							= Repository(local_dir=self._local_dir      ,							clone_from=self._model_name    )

        # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
        SCREAMING_SNAKE_CASE__       							= AutoConfig.from_pretrained(self._local_dir    )
        SCREAMING_SNAKE_CASE__       							= config.architectures
        if self._override_model_class is not None:
            if self._override_model_class.startswith('''TF'''    ):
                SCREAMING_SNAKE_CASE__       							= [self._override_model_class[2:]]
            else:
                SCREAMING_SNAKE_CASE__       							= [self._override_model_class]
            try:
                SCREAMING_SNAKE_CASE__       							= getattr(import_module('''transformers'''    )      ,							architectures[0]    )
            except AttributeError:
                raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''    )
            try:
                SCREAMING_SNAKE_CASE__       							= getattr(import_module('''transformers'''    )      ,							'''TF''' + architectures[0]    )
            except AttributeError:
                raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.'''    )
        elif architectures is None:  # No architecture defined -- use auto classes
            SCREAMING_SNAKE_CASE__       							= getattr(import_module('''transformers'''    )      ,							'''AutoModel'''    )
            SCREAMING_SNAKE_CASE__       							= 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]}'''    )
            SCREAMING_SNAKE_CASE__       							= getattr(import_module('''transformers'''    )      ,							architectures[0]    )
            try:
                SCREAMING_SNAKE_CASE__       							= getattr(import_module('''transformers'''    )      ,							'''TF''' + architectures[0]    )
            except AttributeError:
                raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''    )

        # Check the TF dummy inputs to see what keys we need in the forward pass
        SCREAMING_SNAKE_CASE__       							= tf_class.from_config(A_    )
        SCREAMING_SNAKE_CASE__       							= tf_from_pt_model.dummy_inputs

        del tf_from_pt_model  # Try to keep only one model in memory at a time

        # Load the model and get some basic inputs
        SCREAMING_SNAKE_CASE__       							= pt_class.from_pretrained(self._local_dir    )
        pt_model.eval()

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= self.get_inputs(A_      ,							A_      ,							A_    )

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

        SCREAMING_SNAKE_CASE__       							= tf_class.from_pretrained(self._local_dir      ,							from_pt=A_    )
        SCREAMING_SNAKE_CASE__       							= tf_from_pt_model(**A_      ,							output_hidden_states=A_      ,							training=A_    )

        # Confirms that cross loading PT weights into TF worked.
        SCREAMING_SNAKE_CASE__       							= self.find_pt_tf_differences(A_      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= {k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
        SCREAMING_SNAKE_CASE__       							= {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\''''    )
        SCREAMING_SNAKE_CASE__       							= max(output_differences.values()    ) if output_differences else 0.0
        SCREAMING_SNAKE_CASE__       							= max(hidden_differences.values()    )
        if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
            raise ValueError(
                '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
                + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
                + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]    )
                + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
                + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]    )    )

        # Save the weights in a TF format (if needed) and confirms that the results are still good
        SCREAMING_SNAKE_CASE__       							= os.path.join(self._local_dir      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= 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

        SCREAMING_SNAKE_CASE__       							= tf_class.from_pretrained(self._local_dir    )
        SCREAMING_SNAKE_CASE__       							= tf_model(**A_      ,							output_hidden_states=A_    )

        SCREAMING_SNAKE_CASE__       							= self.find_pt_tf_differences(A_      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
        SCREAMING_SNAKE_CASE__       							= {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\''''    )
        SCREAMING_SNAKE_CASE__       							= max(output_differences.values()    ) if output_differences else 0.0
        SCREAMING_SNAKE_CASE__       							= max(hidden_differences.values()    )
        if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
            raise ValueError(
                '''The converted TensorFlow model has different outputs, something went wrong!\n'''
                + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
                + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]    )
                + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
                + '''\n'''.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]    )    )

        SCREAMING_SNAKE_CASE__       							= '''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...'''    )
            SCREAMING_SNAKE_CASE__       							= (
                '''Model converted by the [`transformers`\' `pt_to_tf`'''
                ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
                '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
                f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
                f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
                f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
                f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
            )
            if self._max_error > MAX_ERROR:
                commit_descrition += (
                    f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
                )
            if self._extra_commit_description:
                commit_descrition += "\n\n" + self._extra_commit_description

            # sharded model -> adds all related files (index and .h5 shards)
            if os.path.exists(A_    ):
                SCREAMING_SNAKE_CASE__       							= [
                    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:
                SCREAMING_SNAKE_CASE__       							= [CommitOperationAdd(path_in_repo=A_      ,							path_or_fileobj=A_    )]

            SCREAMING_SNAKE_CASE__       							= 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}'''    )







import numpy as np

_A				:							List[Any]							= [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= np.array(A_    )



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.where(letter == self.SQUARE    )
        SCREAMING_SNAKE_CASE__       							= np.concatenate([indexa + 1, indexa + 1]    )
        return indexes



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        return self.SQUARE[indexa - 1, indexa - 1]



    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.lower()
        SCREAMING_SNAKE_CASE__       							= message.replace('''j'''      ,							'''i'''    )

        SCREAMING_SNAKE_CASE__       							= ''''''
        for letter_index in range(len(A_    )    ):
            if message[letter_index] != " ":
                SCREAMING_SNAKE_CASE__       							= self.letter_to_numbers(message[letter_index]    )
                SCREAMING_SNAKE_CASE__       							= encoded_message + str(numbers[0]    ) + str(numbers[1]    )
            elif message[letter_index] == " ":
                SCREAMING_SNAKE_CASE__       							= encoded_message + ''' '''

        return encoded_message




    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= message.replace(''' '''      ,							'''  '''    )
        SCREAMING_SNAKE_CASE__       							= ''''''
        for numbers_index in range(int(len(A_    ) / 2    )    ):
            if message[numbers_index * 2] != " ":
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2]
                SCREAMING_SNAKE_CASE__       							= message[numbers_index * 2 + 1]

                SCREAMING_SNAKE_CASE__       							= self.numbers_to_letter(int(A_    )      ,							int(A_    )    )
                SCREAMING_SNAKE_CASE__       							= decoded_message + letter
            elif message[numbers_index * 2] == " ":
                SCREAMING_SNAKE_CASE__       							= decoded_message + ''' '''

        return decoded_message
