




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


_lowerCAmelCase    : List[Any]		= 5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors



def     lowerCAmelCase		(		_lowerCAmelCase				: Namespace						):




 """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   (					lowerCAmelCase		):
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :ArgumentParser   )  ->      List[Any]:
       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=lowerCamelCase			,   required=lowerCamelCase			,   help="The model name, including owner/organization, as seen on the hub."			,   )
       train_parser.add_argument(
           "--local-dir"			,   type=lowerCamelCase			,   default=""			,   help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"			,   )
       train_parser.add_argument(
           "--max-error"			,   type=lowerCamelCase			,   default=lowerCamelCase			,   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=lowerCamelCase			,   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=lowerCamelCase			,   default=lowerCamelCase			,   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=lowerCamelCase   )
      @staticmethod
      def 						UpperCAmelCase_    (      lowerCamelCase :Tuple			,   lowerCamelCase :Tuple   )  ->      int:
       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(lowerCamelCase :Optional[Any]			,   lowerCamelCase :int			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]=""   ):
        # 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(lowerCamelCase			,   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(lowerCamelCase   ):
          # If it is a named attribute, we keep the name. Otherwise, just its index.
          if isinstance(lowerCamelCase			,   lowerCamelCase   ):
           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(lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase   )

        return differences

       return _find_pt_tf_differences(lowerCamelCase			,   lowerCamelCase			,   {}   )
      def __init__(      self :Dict			,   lowerCamelCase :str			,   lowerCamelCase :str			,   lowerCamelCase :float			,   lowerCamelCase :bool			,   lowerCamelCase :bool			,   lowerCamelCase :bool			,   lowerCamelCase :str			,   lowerCamelCase :str			,   *lowerCamelCase :Optional[int]			,   )  ->      int:
       UpperCAmelCase__							=    logging.get_logger("transformers-cli/pt_to_tf"   )
       UpperCAmelCase__							=    model_name
       UpperCAmelCase__							=    local_dir if local_dir else os.path.join("/tmp"			,   lowerCamelCase   )
       UpperCAmelCase__							=    max_error
       UpperCAmelCase__							=    new_weights
       UpperCAmelCase__							=    no_pr
       UpperCAmelCase__							=    push
       UpperCAmelCase__							=    extra_commit_description
       UpperCAmelCase__							=    override_model_class
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :str			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :List[str]   )  ->      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(**lowerCamelCase			,   return_tensors="pt"   )
       UpperCAmelCase__							=    processor(**lowerCamelCase			,   return_tensors="tf"   )

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

       return pt_input, tf_input







      def 						UpperCAmelCase_    (      self :Union[str, Any]   )  ->      Any:
       # 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(lowerCamelCase   ) > 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(lowerCamelCase   )
       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(lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase   )

       with torch.no_grad():
        UpperCAmelCase__							=    pt_model(**lowerCamelCase			,   output_hidden_states=lowerCamelCase   )
       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=lowerCamelCase   )
       UpperCAmelCase__							=    tf_from_pt_model(**lowerCamelCase			,   output_hidden_states=lowerCamelCase			,   training=lowerCamelCase   )

       # Confirms that cross loading PT weights into TF worked.
       UpperCAmelCase__							=    self.find_pt_tf_differences(lowerCamelCase			,   lowerCamelCase   )
       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(lowerCamelCase   ) == 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			,   lowerCamelCase   )
       UpperCAmelCase__							=    os.path.join(self._local_dir			,   lowerCamelCase   )
       if (not os.path.exists(lowerCamelCase   ) and not os.path.exists(lowerCamelCase   )) 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(**lowerCamelCase			,   output_hidden_states=lowerCamelCase   )

       UpperCAmelCase__							=    self.find_pt_tf_differences(lowerCamelCase			,   lowerCamelCase   )
       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(lowerCamelCase   ) == 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=lowerCamelCase   )
        repo.git_commit(lowerCamelCase   )
        repo.git_push(blocking=lowerCamelCase   )  # 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(lowerCamelCase   ):
         UpperCAmelCase__							=    [
             CommitOperationAdd(path_in_repo=lowerCamelCase			,   path_or_fileobj=lowerCamelCase   )
         ]
         for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"   ):
          operations += [
              CommitOperationAdd(path_in_repo=os.path.basename(lowerCamelCase   )			,   path_or_fileobj=lowerCamelCase   )
          ]
        else:
         UpperCAmelCase__							=    [CommitOperationAdd(path_in_repo=lowerCamelCase			,   path_or_fileobj=lowerCamelCase   )]

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











from math import cos, sin



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__							=    float(_lowerCAmelCase						)
 return [[scaling_factor * int(x == y						) for x in range(2						)] for y in range(2						)]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 return [[c, -s], [s, c]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[c * c, cs], [cs, s * s]]



def     lowerCAmelCase		(		_lowerCAmelCase				: float						):




 """simple docstring"""







 UpperCAmelCase__			,			UpperCAmelCase__							=    cos(_lowerCAmelCase						), sin(_lowerCAmelCase						)
 UpperCAmelCase__							=    c * s
 return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'''    {scaling(5) = }''')
print(F'''  {rotation(4_5) = }''')
print(F'''{projection(4_5) = }''')


print(F'''{reflection(4_5) = }''')





