


import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


A				:   Union[str, Any]		= "1.5.0"

A				:   int		= ["vision-encoder-decoder"]




def 				lowercase_		(		_A      :   Union[str, Any]						):

      """simple docstring"""




      if is_optimum_available():
            from optimum.version import __version__ as optimum_version

            lowerCamelCase__		:    Dict   =  version.parse(_A						)
            if parsed_optimum_version < version.parse(_A						):
                  raise RuntimeError(
                      F"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
                      "can upgrade optimum by running: pip install -U optimum[exporters]"						)
      else:
            raise RuntimeError(
                "transformers.onnx requires optimum to run, you can install the library by running: pip install "
                "optimum[exporters]"						)
      lowerCamelCase__		:    Any   =  [
          sys.executable,
          "-m",
          "optimum.exporters.onnx",
          F"--model {args.model}",
          F"--task {args.feature}",
          F"--framework {args.framework}" if args.framework is not None else "",
          F"{args.output}",
      ]
      lowerCamelCase__		:    Optional[int]   =  subprocess.Popen(" ".join(_A						)   , stdout=subprocess.PIPE   , shell=_A						)
      proc.wait()

      logger.info(
          "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
          "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
          "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."						)




def 				lowercase_		(		_A      :   List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:    Dict   =  args.output if args.output.is_file() else args.output.joinpath("model.onnx"						)
      if not args.output.parent.exists():
            args.output.parent.mkdir(parents=_A						)

      # Allocate the model
      lowerCamelCase__		:    int   =  FeaturesManager.get_model_from_feature(
          args.feature   , args.model   , framework=args.framework   , cache_dir=args.cache_dir						)

      lowerCamelCase__		:    Union[str, Any]   =  FeaturesManager.check_supported_model_or_raise(_A   , feature=args.feature						)
      lowerCamelCase__		:    str   =  model_onnx_config(model.config						)

      if model_kind in ENCODER_DECODER_MODELS:
            lowerCamelCase__		:    str   =  model.get_encoder()
            lowerCamelCase__		:    Union[str, Any]   =  model.get_decoder()

            lowerCamelCase__		:    Any   =  onnx_config.get_encoder_config(encoder_model.config						)
            lowerCamelCase__		:    List[str]   =  onnx_config.get_decoder_config(
                encoder_model.config   , decoder_model.config   , feature=args.feature						)

            if args.opset is None:
                  lowerCamelCase__		:    List[Any]   =  max(encoder_onnx_config.default_onnx_opset   , decoder_onnx_config.default_onnx_opset						)

            if args.opset < min(encoder_onnx_config.default_onnx_opset   , decoder_onnx_config.default_onnx_opset						):
                  raise ValueError(
                      F"Opset {args.opset} is not sufficient to export {model_kind}. At least "
                      F" {min(encoder_onnx_config.default_onnx_opset   , decoder_onnx_config.default_onnx_opset						)} is required."						)

            lowerCamelCase__		:    int   =  AutoFeatureExtractor.from_pretrained(args.model						)

            lowerCamelCase__		:    Dict   =  export(
                _A   , _A   , _A   , args.opset   , args.output.parent.joinpath("encoder_model.onnx"						)   , )

            validate_model_outputs(
                _A   , _A   , _A   , args.output.parent.joinpath("encoder_model.onnx"						)   , _A   , args.atol if args.atol else encoder_onnx_config.atol_for_validation   , )

            lowerCamelCase__		:    Union[str, Any]   =  AutoTokenizer.from_pretrained(args.model						)

            lowerCamelCase__		:    Tuple   =  export(
                _A   , _A   , _A   , args.opset   , args.output.parent.joinpath("decoder_model.onnx"						)   , )

            validate_model_outputs(
                _A   , _A   , _A   , args.output.parent.joinpath("decoder_model.onnx"						)   , _A   , args.atol if args.atol else decoder_onnx_config.atol_for_validation   , )
            logger.info(
                F"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'						).as_posix()},"
                F" {args.output.parent.joinpath('decoder_model.onnx'						).as_posix()}"						)

      else:
            # Instantiate the appropriate preprocessor
            if args.preprocessor == "auto":
                  lowerCamelCase__		:    Any   =  get_preprocessor(args.model						)
            elif args.preprocessor == "tokenizer":
                  lowerCamelCase__		:    Union[str, Any]   =  AutoTokenizer.from_pretrained(args.model						)
            elif args.preprocessor == "image_processor":
                  lowerCamelCase__		:    Any   =  AutoImageProcessor.from_pretrained(args.model						)
            elif args.preprocessor == "feature_extractor":
                  lowerCamelCase__		:    Dict   =  AutoFeatureExtractor.from_pretrained(args.model						)
            elif args.preprocessor == "processor":
                  lowerCamelCase__		:    List[str]   =  AutoProcessor.from_pretrained(args.model						)
            else:
                  raise ValueError(F"Unknown preprocessor type '{args.preprocessor}'"						)

            # Ensure the requested opset is sufficient
            if args.opset is None:
                  lowerCamelCase__		:    int   =  onnx_config.default_onnx_opset

            if args.opset < onnx_config.default_onnx_opset:
                  raise ValueError(
                      F"Opset {args.opset} is not sufficient to export {model_kind}. "
                      F"At least  {onnx_config.default_onnx_opset} is required."						)

            lowerCamelCase__		:    Any   =  export(
                _A   , _A   , _A   , args.opset   , args.output   , )

            if args.atol is None:
                  lowerCamelCase__		:    str   =  onnx_config.atol_for_validation

            validate_model_outputs(_A   , _A   , _A   , args.output   , _A   , args.atol						)
            logger.info(F"All good, model saved at: {args.output.as_posix()}"						)
            warnings.warn(
                "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
                " using optimum.exporters.onnx in future. You can find more information here:"
                " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."   , _A   , )




def 				lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:    str   =  ArgumentParser("Hugging Face Transformers ONNX exporter"						)
      parser.add_argument(
          "-m"   , "--model"   , type=_A   , required=_A   , help="Model ID on huggingface.co or path on disk to load model from."						)
      parser.add_argument(
          "--feature"   , default="default"   , help="The type of features to export the model with."   , )
      parser.add_argument("--opset"   , type=_A   , default=_A   , help="ONNX opset version to export the model with."						)
      parser.add_argument(
          "--atol"   , type=_A   , default=_A   , help="Absolute difference tolerance when validating the model."						)
      parser.add_argument(
          "--framework"   , type=_A   , choices=["pt", "tf"]   , default=_A   , help=(
              "The framework to use for the ONNX export."
              " If not provided, will attempt to use the local checkpoint's original framework"
              " or what is available in the environment."
          )   , )
      parser.add_argument("output"   , type=_A   , help="Path indicating where to store generated ONNX model."						)
      parser.add_argument("--cache_dir"   , type=_A   , default=_A   , help="Path indicating where to store cache."						)
      parser.add_argument(
          "--preprocessor"   , type=_A   , choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]   , default="auto"   , help="Which type of preprocessor to use. 'auto' tries to automatically detect it."   , )
      parser.add_argument(
          "--export_with_transformers"   , action="store_true"   , help=(
              "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
              "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
              "recommended."
          )   , )

      lowerCamelCase__		:    str   =  parser.parse_args()
      if args.export_with_transformers or not is_optimum_available():
            export_with_transformers(_A						)
      else:
            export_with_optimum(_A						)


if __name__ == "__main__":
    A				:   Optional[int]		= logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
    logger.setLevel(logging.INFO)
    main()






import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


A				:   Union[str, Any]		= random.Random()

def 				lowercase_		(		_A      :   Any   , _A      :   int=1.0   , _A      :   Optional[int]=None   , _A      :   Union[str, Any]=None						):

      """simple docstring"""




      if rng is None:
            lowerCamelCase__		:    Any   =  global_rng

      lowerCamelCase__		:    Optional[Any]   =  []
      for batch_idx in range(shape[0]						):
            values.append([]						)
            for _ in range(shape[1]						):
                  values[-1].append(rng.random() * scale						)

      return values







@require_torch
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Union[str, Any]=7 ,				__lowerCamelCase							:      int=400 ,				__lowerCamelCase							:      Tuple=2000 ,				__lowerCamelCase							:      int=1 ,				__lowerCamelCase							:      int=0.0 ,				__lowerCamelCase							:      int=24000 ,				__lowerCamelCase							:      Union[str, Any]=True ,				):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  parent
             lowerCamelCase__		:    Union[str, Any]   =  batch_size
             lowerCamelCase__		:    str   =  min_seq_length
             lowerCamelCase__		:    int   =  max_seq_length
             lowerCamelCase__		:    List[Any]   =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             lowerCamelCase__		:    List[str]   =  feature_size
             lowerCamelCase__		:    List[Any]   =  padding_value
             lowerCamelCase__		:    Union[str, Any]   =  sampling_rate
             lowerCamelCase__		:    Tuple   =  return_attention_mask





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
             }





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Tuple=False ,				__lowerCamelCase							:      Optional[int]=False	):


             '''simple docstring'''



             def _flatten(__lowerCamelCase							:      Dict	):
                   return list(itertools.chain(*__lowerCamelCase	)	)

             if equal_length:
                   lowerCamelCase__		:    List[Any]   =  floats_list((self.batch_size, self.max_seq_length)	)
             else:
                   # make sure that inputs increase in size
                   lowerCamelCase__		:    Dict   =  [
                       _flatten(floats_list((x, self.feature_size)	)	)
                       for x in range(self.min_seq_length ,				self.max_seq_length ,				self.seq_length_diff	)
                   ]

             if numpify:
                   lowerCamelCase__		:    Optional[int]   =  [np.asarray(__lowerCamelCase	) for x in audio_inputs]

             return audio_inputs







@require_torch
class       _lowercase			(							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      EncodecFeatureExtractor





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  EnCodecFeatureExtractionTester(self	)





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             # create three inputs of length 800, 1000, and 1200
             lowerCamelCase__		:    Union[str, Any]   =  [floats_list((1, x)	)[0] for x in range(800 ,				1400 ,				200	)]
             lowerCamelCase__		:    Dict   =  [np.asarray(__lowerCamelCase	) for audio_input in audio_inputs]

             # Test not batched input
             lowerCamelCase__		:    Dict   =  feat_extract(audio_inputs[0] ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Optional[int]   =  feat_extract(np_audio_inputs[0] ,				return_tensors="np"	).input_values
             self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)

             # Test batched
             lowerCamelCase__		:    Dict   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Any   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             for enc_seq_a, enc_seq_a in zip(__lowerCamelCase ,				__lowerCamelCase	):
                   self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             lowerCamelCase__		:    Tuple   =  np.random.rand(100	).astype(np.floataa	)
             lowerCamelCase__		:    str   =  np_audio_inputs.tolist()

             for inputs in [py_audio_inputs, np_audio_inputs]:
                   lowerCamelCase__		:    List[Any]   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="np"	)
                   self.assertTrue(np_processed.input_values.dtype == np.floataa	)
                   lowerCamelCase__		:    int   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="pt"	)
                   self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)





       def 						lowerCAmelCase					(       self							:      str ,				__lowerCamelCase							:      Any	):


             '''simple docstring'''



             from datasets import load_dataset

             lowerCamelCase__		:    List[Any]   =  load_dataset("hf-internal-testing/librispeech_asr_dummy" ,				"clean" ,				split="validation"	)
             # automatic decoding with librispeech
             lowerCamelCase__		:    Union[str, Any]   =  ds.sort("id"	).select(range(__lowerCamelCase	)	)[:num_samples]["audio"]

             return [x["array"] for x in audio_samples]





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    str   =  self._load_datasamples(1	)
             lowerCamelCase__		:    Any   =  EncodecFeatureExtractor()
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 1, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    Any   =  self._load_datasamples(1	)
             lowerCamelCase__		:    int   =  [np.tile(input_audio[0][None] ,				reps=(2, 1)	)]
             input_audio[0][1] *= 0.5
             lowerCamelCase__		:    List[str]   =  EncodecFeatureExtractor(feature_size=2	)
             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 2, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)
             self.assertTrue(torch.allclose(input_values[0, 1, :30] ,				EXPECTED_INPUT_VALUES * 0.5 ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self._load_datasamples(2	)
             # would be easier if the stride was like
             lowerCamelCase__		:    Optional[Any]   =  EncodecFeatureExtractor(feature_size=1 ,				chunk_length_s=1 ,				overlap=0.0_1	)

             # pad and trunc raise an error ?
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Both padding and truncation were set. Make sure you only set one.$" ,				):
                   lowerCamelCase__		:    str   =  feature_extractor(
                       __lowerCamelCase ,				padding="max_length" ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values

             # truncate to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 71520)	)  # 2 chunks

             # force truncate to max_length
             lowerCamelCase__		:    List[str]   =  feature_extractor(
                 __lowerCamelCase ,				truncation=__lowerCamelCase ,				max_length=48000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 48000)	)

             # pad to chunk
             lowerCamelCase__		:    int   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(padded_outputs.shape ,				(2, 1, 95280)	)

             # pad to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 95280)	)

             # force pad to max length
             lowerCamelCase__		:    Tuple   =  feature_extractor(
                 __lowerCamelCase ,				padding="max_length" ,				max_length=100000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 100000)	)

             # force no pad
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    str   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no chunk_length_s
             lowerCamelCase__		:    Tuple   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    List[str]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Tuple   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no overlap
             lowerCamelCase__		:    List[Any]   =  2
             lowerCamelCase__		:    List[Any]   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)


