

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

import huggingface_hub
import numpy as np
from packaging import version

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


if is_tf_available():
					import tensorflow as tf

					tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
					import torch

if is_datasets_available():
					from datasets import load_dataset


lowercase     :       Optional[int]        =       5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def  UpperCAmelCase_   (_lowerCAmelCase :		Namespace		):
			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 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      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 __lowerCamelCase					(				__UpperCamelCase , __UpperCamelCase    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      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(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=""    ):
												# 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    ):
															__UpperCamelCase    :    Optional[int]								=      np.max(np.abs(pt_out.numpy() - tf_out.numpy()    )    )
															__UpperCamelCase    :    int								=      tensor_difference
												else:
															__UpperCamelCase    :    Union[str, Any]								=      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    ):
																					__UpperCamelCase    :    List[str]								=      root_name + pt_item
																					__UpperCamelCase    :    Optional[int]								=      tf_out[pt_item]
																					__UpperCamelCase    :    Tuple								=      pt_out[pt_item]
																		else:
																					__UpperCamelCase    :    Optional[Any]								=      root_name + f'''[{i}]'''
																					__UpperCamelCase    :    Optional[Any]								=      tf_out[i]
																		__UpperCamelCase    :    Union[str, Any]								=      _find_pt_tf_differences(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase    )

												return differences

									return _find_pt_tf_differences(__UpperCamelCase , __UpperCamelCase , {}    )

						def __init__(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , *__UpperCamelCase , )							->	int:




									'''simple docstring'''


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

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




									'''simple docstring'''


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

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

									__UpperCamelCase    :    Dict								=      set(inspect.signature(pt_model.forward    ).parameters.keys()    )
									__UpperCamelCase    :    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    :    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    :    Tuple								=      feature_extractor_signature["padding"].default
															if default_strategy is not False and default_strategy is not None:
																		__UpperCamelCase    :    Optional[int]								=      default_strategy
															else:
																		__UpperCamelCase    :    Optional[Any]								=      True
												else:
															__UpperCamelCase    :    int								=      True
												processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}    )
									if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
												processor_inputs.update({"audio": _get_audio_input(), "padding": True}    )
									__UpperCamelCase    :    Optional[Any]								=      processor(**__UpperCamelCase , return_tensors="pt"    )
									__UpperCamelCase    :    List[str]								=      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
									):
												__UpperCamelCase    :    List[Any]								=      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 __lowerCamelCase					(				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
									__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    :    int								=      config.architectures
									if self._override_model_class is not None:
												if self._override_model_class.startswith("TF"    ):
															__UpperCamelCase    :    Optional[Any]								=      [self._override_model_class[2:]]
												else:
															__UpperCamelCase    :    str								=      [self._override_model_class]
												try:
															__UpperCamelCase    :    int								=      getattr(import_module("transformers"    ) , architectures[0]    )
												except AttributeError:
															raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''    )
												try:
															__UpperCamelCase    :    Optional[int]								=      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    :    List[str]								=      getattr(import_module("transformers"    ) , "AutoModel"    )
												__UpperCamelCase    :    Dict								=      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]}'''    )
												__UpperCamelCase    :    List[Any]								=      getattr(import_module("transformers"    ) , architectures[0]    )
												try:
															__UpperCamelCase    :    Union[str, Any]								=      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    :    Tuple								=      tf_class.from_config(__UpperCamelCase    )
									__UpperCamelCase    :    Tuple								=      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    :    Optional[Any]								=      pt_class.from_pretrained(self._local_dir    )
									pt_model.eval()

									__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      self.get_inputs(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase    )

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

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

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

									__UpperCamelCase    :    Optional[Any]								=      tf_class.from_pretrained(self._local_dir    )
									__UpperCamelCase    :    Any								=      tf_model(**__UpperCamelCase , output_hidden_states=__UpperCamelCase    )

									__UpperCamelCase    :    Tuple								=      self.find_pt_tf_differences(__UpperCamelCase , __UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      {k: v for k, v in conversion_differences.items() if "hidden" not in k}
									__UpperCamelCase    :    Optional[Any]								=      {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'"    )
									__UpperCamelCase    :    List[Any]								=      max(output_differences.values()    ) if output_differences else 0.0
									__UpperCamelCase    :    Tuple								=      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    :    Dict								=      "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..."    )
												__UpperCamelCase    :    List[Any]								=      (
												    "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    ):
															__UpperCamelCase    :    List[str]								=      [
															    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:
															__UpperCamelCase    :    Union[str, Any]								=      [CommitOperationAdd(path_in_repo=__UpperCamelCase , path_or_fileobj=__UpperCamelCase    )]

												__UpperCamelCase    :    List[str]								=      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}'''    )


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase     :       Dict        =       "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase						,	lowerCamelCase__   ):







						"""simple docstring"""




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




									'''simple docstring'''


									__UpperCamelCase    :    str								=      load_tool("summarization"    )
									self.tool.setup()
									__UpperCamelCase    :    str								=      load_tool("summarization" , remote=__UpperCamelCase    )

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




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      self.tool(__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM." , )

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      self.remote_tool(__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM." , )

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




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      self.tool(text=__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM." , )




						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      self.remote_tool(text=__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM." , )