



'''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


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







def _lowercase   (  __A       ):



 '''simple docstring'''



 return PTtoTFCommand(
     args.model_name							,args.local_dir							,args.max_error							,args.new_weights							,args.no_pr							,args.push							,args.extra_commit_description							,args.override_model_class							,)






class        UpperCAmelCase__  (    UpperCAmelCase_):



 @staticmethod
 def    __lowerCamelCase				(  lowercase			)  ->							Dict:
  __UpperCamelCase				      =  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=lowercase		,	required=lowercase		,	help="""The model name, including owner/organization, as seen on the hub."""		,	)
  train_parser.add_argument(
      """--local-dir"""		,	type=lowercase		,	default=""""""		,	help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""		,	)
  train_parser.add_argument(
      """--max-error"""		,	type=lowercase		,	default=lowercase		,	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=lowercase		,	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=lowercase		,	default=lowercase		,	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=lowercase			)



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

   return differences

  return _find_pt_tf_differences(lowercase		,	lowercase		,	{}			)



 def __init__(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	*lowercase		,	)  ->							Optional[Any]:
  __UpperCamelCase				      =  logging.get_logger("""transformers-cli/pt_to_tf"""			)
  __UpperCamelCase				      =  model_name
  __UpperCamelCase				      =  local_dir if local_dir else os.path.join("""/tmp"""		,	lowercase			)
  __UpperCamelCase				      =  max_error
  __UpperCamelCase				      =  new_weights
  __UpperCamelCase				      =  no_pr
  __UpperCamelCase				      =  push
  __UpperCamelCase				      =  extra_commit_description
  __UpperCamelCase				      =  override_model_class



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Any:

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

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

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

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

  return pt_input, tf_input




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

  __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_inputs(lowercase		,	lowercase		,	lowercase			)

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

  __UpperCamelCase				      =  tf_class.from_pretrained(self._local_dir		,	from_pt=lowercase			)
  __UpperCamelCase				      =  tf_from_pt_model(**lowercase		,	output_hidden_states=lowercase		,	training=lowercase			)

  # Confirms that cross loading PT weights into TF worked.
  __UpperCamelCase				      =  self.find_pt_tf_differences(lowercase		,	lowercase			)
  __UpperCamelCase				      =  {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
  __UpperCamelCase				      =  {k: v for k, v in crossload_differences.items() if """hidden""" in k}
  if len(lowercase			) == 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				      =  max(output_differences.values()			) if output_differences else 0.0
  __UpperCamelCase				      =  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				      =  os.path.join(self._local_dir		,	lowercase			)
  __UpperCamelCase				      =  os.path.join(self._local_dir		,	lowercase			)
  if (not os.path.exists(lowercase			) and not os.path.exists(lowercase			)) 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				      =  tf_class.from_pretrained(self._local_dir			)
  __UpperCamelCase				      =  tf_model(**lowercase		,	output_hidden_states=lowercase			)

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

   __UpperCamelCase				      =  create_commit(
       repo_id=self._model_name		,	operations=lowercase		,	commit_message=lowercase		,	commit_description=lowercase		,	repo_type="""model"""		,	create_pr=lowercase		,	).pr_url
   self._logger.warning(f"PR open in {hub_pr_url}"			)










'''simple docstring'''



import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil







@pytest.fixture
def _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  tmp_path / """text.txt"""
 __UpperCamelCase				      =  textwrap.dedent(
     """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """       )
 with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
  f.write(__A       )
 return str(__A       )







@pytest.fixture
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
 with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
  f.write(__A       )
 return str(__A       )







@pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
 __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
 __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
 assert generated_content == expected_content







@require_pil
def _lowercase   (  __A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read().splitlines()[0]
 __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
 __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
 assert pa_table.schema.field("""image"""       ).type == Image()()
 __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
 assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read()
 if sample_by == "line":
  __UpperCamelCase				      =  expected_content.splitlines()
 elif sample_by == "paragraph":
  __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
 elif sample_by == "document":
  __UpperCamelCase				      =  [expected_content]
 __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
 __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
 assert generated_content == expected_content





