

"""simple docstring"""



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

import huggingface_hub
import numpy as np
from packaging import version

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


if is_tf_available():
 import tensorflow as tf

 tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset


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





def 			_snake_case  (     UpperCAmelCase_	:       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    (							__snake_case					):



    """simple docstring"""



    @staticmethod
    def 					UpperCamelCase						(  UpperCamelCase:			List[Any]							):






         """simple docstring"""


         A__				      =	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 					UpperCamelCase						(  UpperCamelCase:			int     ,    UpperCamelCase:			int							):






         """simple docstring"""


         A__				      =	set(pt_outputs.keys()							)
         A__				      =	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[Any]     ,    UpperCamelCase:			Dict     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Dict=""							):
              # 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							):
                   A__				      =	np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
                   A__				      =	tensor_difference
              else:
                   A__				      =	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							):
                             A__				      =	root_name + pt_item
                             A__				      =	tf_out[pt_item]
                             A__				      =	pt_out[pt_item]
                        else:
                             A__				      =	root_name + f"""[{i}]"""
                             A__				      =	tf_out[i]
                        A__				      =	_find_pt_tf_differences(UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase							)

              return differences

         return _find_pt_tf_differences(UpperCamelCase     ,    UpperCamelCase     ,    {}							)

    def __init__(  self:			Tuple     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			str     ,    UpperCamelCase:			List[str]     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Union[str, Any]     ,    UpperCamelCase:			Dict     ,    UpperCamelCase:			int     ,    UpperCamelCase:			int     ,    *UpperCamelCase:			Any     ,    ):






         """simple docstring"""


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

    def 					UpperCamelCase						(  self:			str     ,    UpperCamelCase:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			str							):






         """simple docstring"""


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

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

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






         """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
         A__				      =	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
         A__				      =	AutoConfig.from_pretrained(self._local_dir							)
         A__				      =	config.architectures
         if self._override_model_class is not None:
              if self._override_model_class.startswith("""TF"""							):
                   A__				      =	[self._override_model_class[2:]]
              else:
                   A__				      =	[self._override_model_class]
              try:
                   A__				      =	getattr(import_module("""transformers"""							)     ,    architectures[0]							)
              except AttributeError:
                   raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
              try:
                   A__				      =	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
              A__				      =	getattr(import_module("""transformers"""							)     ,    """AutoModel"""							)
              A__				      =	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]}"""							)
              A__				      =	getattr(import_module("""transformers"""							)     ,    architectures[0]							)
              try:
                   A__				      =	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
         A__				      =	tf_class.from_config(UpperCamelCase							)
         A__				      =	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
         A__				      =	pt_class.from_pretrained(self._local_dir							)
         pt_model.eval()

         A__      ,					A__				      =	self.get_inputs(UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase							)

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

         A__				      =	tf_class.from_pretrained(self._local_dir     ,    from_pt=UpperCamelCase							)
         A__				      =	tf_from_pt_model(**UpperCamelCase     ,    output_hidden_states=UpperCamelCase     ,    training=UpperCamelCase							)

         # Confirms that cross loading PT weights into TF worked.
         A__				      =	self.find_pt_tf_differences(UpperCamelCase     ,    UpperCamelCase							)
         A__				      =	{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
         A__				      =	{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'"""							)
         A__				      =	max(output_differences.values()							) if output_differences else 0.0
         A__				      =	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
         A__				      =	os.path.join(self._local_dir     ,    UpperCamelCase							)
         A__				      =	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

         A__				      =	tf_class.from_pretrained(self._local_dir							)
         A__				      =	tf_model(**UpperCamelCase     ,    output_hidden_states=UpperCamelCase							)

         A__				      =	self.find_pt_tf_differences(UpperCamelCase     ,    UpperCamelCase							)
         A__				      =	{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
         A__				      =	{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'"""							)
         A__				      =	max(output_differences.values()							) if output_differences else 0.0
         A__				      =	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]							)							)

         A__				      =	"""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..."""							)
              A__				      =	(
                  """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							):
                   A__				      =	[
                       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:
                   A__				      =	[CommitOperationAdd(path_in_repo=UpperCamelCase     ,    path_or_fileobj=UpperCamelCase							)]

              A__				      =	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}"""							)









from math import pi



def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()