





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


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  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_       ):



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



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

         return differences

       return _find_pt_tf_differences(_snake_case					,			_snake_case					,			{}	)



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



     def      __a    (		self    :    Optional[Any]					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Union[str, Any]	):

       def _get_audio_input():
         lowerCamelCase_  :    Dict   	=			load_dataset('hf-internal-testing/librispeech_asr_dummy'					,			'clean'					,			split='validation'	)
         lowerCamelCase_  :    Union[str, Any]   	=			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_  :    Optional[Any]   	=			AutoProcessor.from_pretrained(self._local_dir	)
         if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
           lowerCamelCase_  :    List[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_  :    Any   	=			AutoTokenizer.from_pretrained(self._local_dir	)
         if processor.pad_token is None:
           lowerCamelCase_  :    Optional[Any]   	=			processor.eos_token
       else:
         raise ValueError(F"Unknown data processing type (model config type: {model_config_class})"	)

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

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

       return pt_input, tf_input





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

       lowerCamelCase_  :    List[Any]   	=			self.get_inputs(_snake_case					,			_snake_case					,			_snake_case	)

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

       lowerCamelCase_  :    Union[str, Any]   	=			tf_class.from_pretrained(self._local_dir					,			from_pt=_snake_case	)
       lowerCamelCase_  :    Tuple   	=			tf_from_pt_model(**_snake_case					,			output_hidden_states=_snake_case					,			training=_snake_case	)

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

       lowerCamelCase_  :    int   	=			self.find_pt_tf_differences(_snake_case					,			_snake_case	)
       lowerCamelCase_  :    str   	=			{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
       lowerCamelCase_  :    List[str]   	=			{k: v for k, v in conversion_differences.items() if '''hidden''' in k}
       if len(_snake_case	) == 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_  :    Any   	=			max(output_differences.values()	) if output_differences else 0.0
       lowerCamelCase_  :    Union[str, 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_  :    Dict   	=			'''Update TF weights''' if self._new_weights else '''Add TF weights'''
       if self._push:
         repo.git_add(auto_lfs_track=_snake_case	)
         repo.git_commit(_snake_case	)
         repo.git_push(blocking=_snake_case	)  # 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_  :    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(_snake_case	):
           lowerCamelCase_  :    int   	=			[
               CommitOperationAdd(path_in_repo=_snake_case					,			path_or_fileobj=_snake_case	)
           ]
           for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'	):
             operations += [
                 CommitOperationAdd(path_in_repo=os.path.basename(_snake_case	)					,			path_or_fileobj=_snake_case	)
             ]
         else:
           lowerCamelCase_  :    str   	=			[CommitOperationAdd(path_in_repo=_snake_case					,			path_or_fileobj=_snake_case	)]

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







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:     Optional[Any] 					=[(1,)]


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow:
				assert True
		else:
				assert False, "should have been skipped"


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow and torch_device == "cuda":
				assert True
		else:
				assert False, "should have been skipped"






@require_torch
class 	UpperCamelCase_     (     unittest.TestCase       ):



					@slow
					@require_torch_gpu
					def      __a    (		self    :    Tuple	):
							check_slow_torch_cuda()



					@require_torch_gpu
					@slow
					def      __a    (		self    :    Optional[int]	):
							check_slow_torch_cuda()





					@parameterized.expand(lowerCamelCase	)
					@slow
					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
							check_slow()


@slow
@require_torch_gpu
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@require_torch_gpu
@slow
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('param'       ,[1]	)
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()





@pytest.mark.parametrize('param'       ,[1]	)
@slow
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()
