






import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
  from comet_ml import OfflineExperiment

if is_tensorboard_available():
  import struct

  import tensorboard.compat.proto.event_pba as event_pba

SCREAMING_SNAKE_CASE								=  logging.getLogger(__name__)
@require_tensorboard
class 							A_  (  unittest.TestCase     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			'test_project_with_config'
     with tempfile.TemporaryDirectory() as dirpath:
         _UpperCAmelCase			: str     					=			Accelerator(log_with='''tensorboard'''							,					project_dir=_A)
         _UpperCAmelCase			: Any     					=			{'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
         accelerator.init_trackers(_A							,					_A)
         accelerator.end_training()
         for child in Path(f'''{dirpath}/{project_name}''').glob('''*/**'''):
             _UpperCAmelCase			: Union[str, Any]     					=			list(filter(lambda _A: x.is_file()							,					child.iterdir()))[0]
         self.assertNotEqual(str(_A)							,					'''''')


 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			'test_project_with_log'
     with tempfile.TemporaryDirectory() as dirpath:
         _UpperCAmelCase			: Union[str, Any]     					=			Accelerator(log_with='''tensorboard'''							,					project_dir=_A)
         accelerator.init_trackers(_A)
         _UpperCAmelCase			: int     					=			{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
         accelerator.log(_A							,					step=0)
         accelerator.end_training()
         # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
         # Names are randomly generated each time
         _UpperCAmelCase			: str     					=			list(filter(lambda _A: x.is_file()							,					Path(f'''{dirpath}/{project_name}''').iterdir()))[0]
         self.assertNotEqual(str(_A)							,					'''''')


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			'test_project_with_log'
     with tempfile.TemporaryDirectory() as dirpath:
         _UpperCAmelCase			: Optional[Any]     					=			Accelerator(log_with='''tensorboard'''							,					project_dir=_A)
         accelerator.init_trackers(_A)
         _UpperCAmelCase			: str     					=			{'tensor': torch.tensor(1)}
         accelerator.log(_A							,					step=0)
         accelerator.end_training()
         # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
         # Names are randomly generated each time
         _UpperCAmelCase			: List[Any]     					=			list(filter(lambda _A: x.is_file()							,					Path(f'''{dirpath}/{project_name}''').iterdir()))[0]
         # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
         with open(_A							,					'''rb''') as f:
             _UpperCAmelCase			: Union[str, Any]     					=			f.read()
         _UpperCAmelCase			: Optional[Any]     					=			False
         while data:
             _UpperCAmelCase			: List[str]     					=			struct.unpack('''Q'''							,					data[:8])

             _UpperCAmelCase			: str     					=			data[12 : 12 + int(header[0])]  # 8+4
             _UpperCAmelCase			: List[Any]     					=			data[12 + int(header[0]) + 4 :]
             _UpperCAmelCase			: List[str]     					=			event_pba.Event()

             event.ParseFromString(_A)
             if event.HasField('''summary'''):
                 for value in event.summary.value:
                     if value.simple_value == 1.0 and value.tag == "tensor":
                         _UpperCAmelCase			: Tuple     					=			True
         self.assertTrue(_A							,					'''Converted tensor was not found in the log file!''')


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     with self.assertRaisesRegex(_A							,					'''Logging with `tensorboard` requires a `logging_dir`'''):
         _UpperCAmelCase			: Union[str, Any]     					=			Accelerator(log_with='''tensorboard''')
     with tempfile.TemporaryDirectory() as dirpath:
         _UpperCAmelCase			: Any     					=			Accelerator(log_with='''tensorboard'''							,					project_dir=_A)


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			ProjectConfiguration(total_limit=30)
     with tempfile.TemporaryDirectory() as dirpath:
         _UpperCAmelCase			: List[str]     					=			Accelerator(log_with='''tensorboard'''							,					project_dir=_A							,					project_config=_A)



@require_wandb
@mock.patch.dict(os.environ	,				{"WANDB_MODE": "offline"}     )
class 							A_  (  snake_case__	,				snake_case__     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



     super().setUp()
     # wandb let's us override where logs are stored to via the WANDB_DIR env var
     self.add_mocks(mock.patch.dict(os.environ							,					{'''WANDB_DIR''': self.tmpdir}))


 @staticmethod
 def 	snake_case__			(		_A							,					_A							,					_A = True)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			Rf'''{section} ([\S\s]*?)\n\n'''
     if record:
         _UpperCAmelCase			: int     					=			Rf'''Record: {pattern}'''
     _UpperCAmelCase			: Optional[int]     					=			re.findall(_A							,					_A)[0]
     # A config
     if section == "config" or section == "history":
         _UpperCAmelCase			: int     					=			re.findall(R'''"([a-zA-Z0-9_.,]+)'''							,					_A)
         return {key: val for key, val in zip(cleaned_record[0::2]							,					cleaned_record[1::2])}
     # Everything else
     else:
         return dict(re.findall(R'''(\w+): "([^\s]+)"'''							,					_A))


 @skip
 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			'test_project_with_config'
     _UpperCAmelCase			: List[Any]     					=			Accelerator(log_with='''wandb''')
     _UpperCAmelCase			: Any     					=			{'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
     _UpperCAmelCase			: str     					=			{'wandb': {'tags': ['my_tag']}}
     accelerator.init_trackers(_A							,					_A							,					_A)
     _UpperCAmelCase			: List[str]     					=			{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
     accelerator.log(_A							,					step=0)
     accelerator.end_training()
     # The latest offline log is stored at wandb/latest-run/*.wandb
     for child in Path(f'''{self.tmpdir}/wandb/latest-run''').glob('''*'''):
         if child.is_file() and child.suffix == ".wandb":
             _UpperCAmelCase			: Optional[Any]     					=			subprocess.check_output(
                 ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(_A)]							,					env=os.environ.copy()).decode('''utf8'''							,					'''ignore''')
             break

             # Check HPS through careful parsing and cleaning
     _UpperCAmelCase			: Any     					=			self.parse_log(_A							,					'''config''')
     self.assertEqual(logged_items['''num_iterations''']							,					'''12''')
     self.assertEqual(logged_items['''learning_rate''']							,					'''0.01''')
     self.assertEqual(logged_items['''some_boolean''']							,					'''false''')
     self.assertEqual(logged_items['''some_string''']							,					'''some_value''')
     self.assertEqual(logged_items['''some_string''']							,					'''some_value''')

     # Run tags
     _UpperCAmelCase			: Dict     					=			self.parse_log(_A							,					'''run'''							,					_A)
     self.assertEqual(logged_items['''tags''']							,					'''my_tag''')

     # Actual logging
     _UpperCAmelCase			: str     					=			self.parse_log(_A							,					'''history''')
     self.assertEqual(logged_items['''total_loss''']							,					'''0.1''')
     self.assertEqual(logged_items['''iteration''']							,					'''1''')
     self.assertEqual(logged_items['''my_text''']							,					'''some_value''')
     self.assertEqual(logged_items['''_step''']							,					'''0''')



def        _lowerCamelCase		(  self      :							str						,						__A      :							str						,						__A      :							str	) ->   Any:
    _UpperCAmelCase			: Union[str, Any]     					=			run_name
    _UpperCAmelCase			: Optional[int]     					=			OfflineExperiment(project_name=__A						,						offline_directory=__A	)
    logger.info(f'''Initialized offline CometML project {self.run_name}'''	)
    logger.info('''Make sure to log any initial configurations with `self.store_init_configuration` before training!'''	)
@require_comet_ml
@mock.patch.object(snake_case__	,				"__init__"	,				snake_case__     )
class 							A_  (  unittest.TestCase     ):






 '''simple docstring'''




 @staticmethod
 def 	snake_case__			(		_A							,					_A							,					_A = False)	->						List[Any]:

     """simple docstring"""



     for log in log_list:
         _UpperCAmelCase			: int     					=			json.loads(_A)['payload']
         if is_param and "param" in j.keys():
             if j["param"]["paramName"] == key:
                 return j["param"]["paramValue"]
         if "log_other" in j.keys():
             if j["log_other"]["key"] == key:
                 return j["log_other"]["val"]
         if "metric" in j.keys():
             if j["metric"]["metricName"] == key:
                 return j["metric"]["metricValue"]


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as d:
         _UpperCAmelCase			: Tuple     					=			CometMLTracker('''test_project_with_config'''							,					_A)
         _UpperCAmelCase			: str     					=			Accelerator(log_with=_A)
         _UpperCAmelCase			: Optional[int]     					=			{'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
         accelerator.init_trackers(_A							,					_A)
         accelerator.end_training()
         _UpperCAmelCase			: Optional[int]     					=			os.listdir(_A)[0]  # Comet is nice, it's just a zip file here
         # We parse the raw logs
         _UpperCAmelCase			: Any     					=			os.path.join(_A							,					_A)
         _UpperCAmelCase			: str     					=			zipfile.ZipFile(_A							,					'''r''')
         _UpperCAmelCase			: Tuple     					=			archive.open('''messages.json''').read().decode('''utf-8''')
     _UpperCAmelCase			: Any     					=			log.split('''\n''')[:-1]
     self.assertEqual(self.get_value_from_key(_A							,					'''num_iterations'''							,					_A)							,					12)
     self.assertEqual(self.get_value_from_key(_A							,					'''learning_rate'''							,					_A)							,					0.01)
     self.assertEqual(self.get_value_from_key(_A							,					'''some_boolean'''							,					_A)							,					_A)
     self.assertEqual(self.get_value_from_key(_A							,					'''some_string'''							,					_A)							,					'''some_value''')


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as d:
         _UpperCAmelCase			: Any     					=			CometMLTracker('''test_project_with_config'''							,					_A)
         _UpperCAmelCase			: Optional[Any]     					=			Accelerator(log_with=_A)
         accelerator.init_trackers(_A)
         _UpperCAmelCase			: Any     					=			{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
         accelerator.log(_A							,					step=0)
         accelerator.end_training()
         _UpperCAmelCase			: List[str]     					=			os.listdir(_A)[0]  # Comet is nice, it's just a zip file here
         # We parse the raw logs
         _UpperCAmelCase			: Optional[int]     					=			os.path.join(_A							,					_A)
         _UpperCAmelCase			: Any     					=			zipfile.ZipFile(_A							,					'''r''')
         _UpperCAmelCase			: Dict     					=			archive.open('''messages.json''').read().decode('''utf-8''')
     _UpperCAmelCase			: Optional[Any]     					=			log.split('''\n''')[:-1]
     self.assertEqual(self.get_value_from_key(_A							,					'''curr_step'''							,					_A)							,					0)
     self.assertEqual(self.get_value_from_key(_A							,					'''total_loss''')							,					0.1)
     self.assertEqual(self.get_value_from_key(_A							,					'''iteration''')							,					1)
     self.assertEqual(self.get_value_from_key(_A							,					'''my_text''')							,					'''some_value''')



class 							A_  (  snake_case__     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   [
     'total_loss',
     'iteration',
     'my_text',
     'learning_rate',
     'num_iterations',
     'some_boolean',
     'some_string',
 ]

 _SCREAMING_SNAKE_CASE							:     List[Any]	=   'my_custom_tracker'
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   False


 def __init__(		self							,					_A)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			open(f'''{dir}/log.csv'''							,					'''w+''')
     _UpperCAmelCase			: Optional[int]     					=			csv.DictWriter(self.f							,					fieldnames=self._col_names)
     self.writer.writeheader()


 @property
 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     return self.writer


 def 	snake_case__			(		self							,					_A)	->						str:

     """simple docstring"""



     logger.info('''Call init''')
     self.writer.writerow(_A)


 def 	snake_case__			(		self							,					_A							,					_A)	->						str:

     """simple docstring"""



     logger.info('''Call log''')
     self.writer.writerow(_A)


 def 	snake_case__			(		self)	->						List[Any]:

     """simple docstring"""



     self.f.close()



class 							A_  (  unittest.TestCase     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as d:
         _UpperCAmelCase			: Optional[int]     					=			MyCustomTracker(_A)
         _UpperCAmelCase			: Tuple     					=			Accelerator(log_with=_A)
         _UpperCAmelCase			: Optional[int]     					=			{'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
         accelerator.init_trackers('''Some name'''							,					_A)
         accelerator.end_training()
         with open(f'''{d}/log.csv'''							,					'''r''') as f:
             _UpperCAmelCase			: int     					=			csv.DictReader(_A)
             _UpperCAmelCase			: List[str]     					=			next(_A)
             _UpperCAmelCase			: List[Any]     					=			{
                 'total_loss': '',
                 'iteration': '',
                 'my_text': '',
                 'learning_rate': '0.01',
                 'num_iterations': '12',
                 'some_boolean': 'False',
                 'some_string': 'some_value',
             }
             self.assertDictEqual(_A							,					_A)


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     with tempfile.TemporaryDirectory() as d:
         _UpperCAmelCase			: List[str]     					=			MyCustomTracker(_A)
         _UpperCAmelCase			: Dict     					=			Accelerator(log_with=_A)
         accelerator.init_trackers('''Some name''')
         _UpperCAmelCase			: Union[str, Any]     					=			{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
         accelerator.log(_A							,					step=0)
         accelerator.end_training()
         with open(f'''{d}/log.csv'''							,					'''r''') as f:
             _UpperCAmelCase			: Union[str, Any]     					=			csv.DictReader(_A)
             _UpperCAmelCase			: Union[str, Any]     					=			next(_A)
             _UpperCAmelCase			: Optional[Any]     					=			{
                 'total_loss': '0.1',
                 'iteration': '1',
                 'my_text': 'some_value',
                 'learning_rate': '',
                 'num_iterations': '',
                 'some_boolean': '',
                 'some_string': '',
             }
             self.assertDictEqual(_A							,					_A)












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


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

def        _lowerCamelCase		(  __A      :							Namespace	) ->   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 							A_      (  __lowercase     ):






 '''simple docstring'''




 @staticmethod
 def 	snake_case__			(		_A)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: 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 	snake_case__			(		_A							,					_A)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			set(pt_outputs.keys())
     _UpperCAmelCase			: 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(_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):
             _UpperCAmelCase			: Tuple     					=			np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
             _UpperCAmelCase			: int     					=			tensor_difference
         else:
             _UpperCAmelCase			: int     					=			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):
                     _UpperCAmelCase			: Union[str, Any]     					=			root_name + pt_item
                     _UpperCAmelCase			: int     					=			tf_out[pt_item]
                     _UpperCAmelCase			: List[Any]     					=			pt_out[pt_item]
                 else:
                     _UpperCAmelCase			: Tuple     					=			root_name + f'''[{i}]'''
                     _UpperCAmelCase			: Optional[Any]     					=			tf_out[i]
                 _UpperCAmelCase			: List[str]     					=			_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							,					)	->						Optional[int]:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						Tuple:

     """simple docstring"""



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

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

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

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

     _UpperCAmelCase					,	_UpperCAmelCase			: Dict     					=			self.get_inputs(_A							,					_A							,					_A)

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

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

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

     _UpperCAmelCase			: List[Any]     					=			tf_class.from_pretrained(self._local_dir)
     _UpperCAmelCase			: Optional[int]     					=			tf_model(**_A							,					output_hidden_states=_A)

     _UpperCAmelCase			: Any     					=			self.find_pt_tf_differences(_A							,					_A)
     _UpperCAmelCase			: str     					=			{k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
     _UpperCAmelCase			: Any     					=			{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\'''')
     _UpperCAmelCase			: List[str]     					=			max(output_differences.values()) if output_differences else 0.0
     _UpperCAmelCase			: 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]))

     _UpperCAmelCase			: List[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...''')
         _UpperCAmelCase			: Dict     					=			(
             '''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):
             _UpperCAmelCase			: int     					=			[
                 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:
             _UpperCAmelCase			: List[str]     					=			[CommitOperationAdd(path_in_repo=_A							,					path_or_fileobj=_A)]

         _UpperCAmelCase			: Any     					=			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}''')




