"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import numpy
       import tensorflow as tf

       from transformers.models.esm.modeling_tf_esm import (
           TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFEsmForMaskedLM,
           TFEsmForSequenceClassification,
           TFEsmForTokenClassification,
           TFEsmModel,
       )





class  _A						:

      def __init__(						self		,       __lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	parent
            lowercase														=	13
            lowercase														=	7
            lowercase														=	True
            lowercase														=	True
            lowercase														=	True
            lowercase														=	99
            lowercase														=	32
            lowercase														=	2
            lowercase														=	4
            lowercase														=	37
            lowercase														=	"""gelu"""
            lowercase														=	0.1
            lowercase														=	0.1
            lowercase														=	512
            lowercase														=	16
            lowercase														=	2
            lowercase														=	0.0_2
            lowercase														=	3
            lowercase														=	4
            lowercase														=	None

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size						)

            lowercase														=	None
            if self.use_input_mask:
                  lowercase														=	random_attention_mask([self.batch_size, self.seq_length]						)

            lowercase														=	None
            lowercase														=	None
            lowercase														=	None
            if self.use_labels:
                  lowercase														=	ids_tensor([self.batch_size]		,       self.type_sequence_label_size						)
                  lowercase														=	ids_tensor([self.batch_size, self.seq_length]		,       self.num_labels						)
                  lowercase														=	ids_tensor([self.batch_size]		,       self.num_choices						)

            lowercase														=	EsmConfig(
                vocab_size=self.vocab_size		,       hidden_size=self.hidden_size		,       num_hidden_layers=self.num_hidden_layers		,       pad_token_id=1		,       num_attention_heads=self.num_attention_heads		,       intermediate_size=self.intermediate_size		,       hidden_act=self.hidden_act		,       hidden_dropout_prob=self.hidden_dropout_prob		,       attention_probs_dropout_prob=self.attention_probs_dropout_prob		,       max_position_embeddings=self.max_position_embeddings		,       type_vocab_size=self.type_vocab_size		,       initializer_range=self.initializer_range		,       )

            return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

      def 				A__       (						self						):







            """simple docstring"""



            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=	self.prepare_config_and_inputs()

            lowercase														=	True
            lowercase														=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
            lowercase														=	ids_tensor([self.batch_size, self.seq_length]		,       vocab_size=2						)

            return (
                config,
                input_ids,
                input_mask,
                sequence_labels,
                token_labels,
                choice_labels,
                encoder_hidden_states,
                encoder_attention_mask,
            )

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	TFEsmModel(config=__lowerCAmelCase						)
            lowercase														=	{"""input_ids""": input_ids, """attention_mask""": input_mask}
            lowercase														=	model(__lowerCAmelCase						)

            lowercase														=	[input_ids, input_mask]
            lowercase														=	model(__lowerCAmelCase						)

            lowercase														=	model(__lowerCAmelCase						)

            self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       ):







            """simple docstring"""



            lowercase														=	True

            lowercase														=	TFEsmModel(config=__lowerCAmelCase						)
            lowercase														=	{
                """input_ids""": input_ids,
                """attention_mask""": input_mask,
                """encoder_hidden_states""": encoder_hidden_states,
                """encoder_attention_mask""": encoder_attention_mask,
            }
            lowercase														=	model(__lowerCAmelCase						)

            lowercase														=	[input_ids, input_mask]
            lowercase														=	model(__lowerCAmelCase		,       encoder_hidden_states=__lowerCAmelCase						)

            # Also check the case where encoder outputs are not passed
            lowercase														=	model(__lowerCAmelCase		,       attention_mask=__lowerCAmelCase						)

            self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	TFEsmForMaskedLM(config=__lowerCAmelCase						)
            lowercase														=	model([input_ids, input_mask]						)
            self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.vocab_size)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.num_labels
            lowercase														=	TFEsmForTokenClassification(config=__lowerCAmelCase						)
            lowercase														=	{"""input_ids""": input_ids, """attention_mask""": input_mask}
            lowercase														=	model(__lowerCAmelCase						)
            self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.num_labels)						)







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.prepare_config_and_inputs()
            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=	config_and_inputs
            lowercase														=	{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict






@require_tf
class  _A						(       lowerCAmelCase  ,    lowerCAmelCase  ,    unittest.TestCase	):
      snake_case__			:   Optional[Any]							  =	(
          (
              TFEsmModel,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      snake_case__			:   Dict							  =	(
          {
              'feature-extraction': TFEsmModel,
              'fill-mask': TFEsmForMaskedLM,
              'text-classification': TFEsmForSequenceClassification,
              'token-classification': TFEsmForTokenClassification,
              'zero-shot': TFEsmForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      snake_case__			:   List[Any]							  =	False
      snake_case__			:   Optional[int]							  =	False

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFEsmModelTester(self						)
            lowercase														=	ConfigTester(self		,       config_class=__lowerCAmelCase		,       hidden_size=37						)

      def 				A__       (						self						):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase						)

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase														=	TFEsmModel.from_pretrained(__lowerCAmelCase						)
                  self.assertIsNotNone(__lowerCAmelCase						)

      @unittest.skip("""Protein models do not support embedding resizing."""						)
      def 				A__       (						self						):







            """simple docstring"""



            pass

      @unittest.skip("""Protein models do not support embedding resizing."""						)
      def 				A__       (						self						):







            """simple docstring"""



            pass







      def 				A__       (						self						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowercase														=	model_class(__lowerCAmelCase						)
                  assert isinstance(model.get_input_embeddings()		,       tf.keras.layers.Layer						)
                  if model_class is TFEsmForMaskedLM:
                        # Output embedding test differs from the main test because they're a matrix, not a layer
                        lowercase														=	model.get_bias()
                        assert isinstance(__lowerCAmelCase		,       __lowerCAmelCase						)
                        for k, v in name.items():
                              assert isinstance(__lowerCAmelCase		,       tf.Variable						)
                  else:
                        lowercase														=	model.get_output_embeddings()
                        assert x is None
                        lowercase														=	model.get_bias()
                        assert name is None






@require_tf
class  _A						(       unittest.TestCase	):

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""						)

            lowercase														=	tf.constant([[0, 1, 2, 3, 4, 5]]						)
            lowercase														=	model(__lowerCAmelCase						)[0]
            lowercase														=	[1, 6, 33]
            self.assertEqual(list(output.numpy().shape						)		,       __lowerCAmelCase						)
            # compare the actual values for a slice.
            lowercase														=	tf.constant(
                [
                    [
                        [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
                        [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
                        [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
                    ]
                ]						)
            self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,       expected_slice.numpy()		,       atol=1E-2						)						)







      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""						)

            lowercase														=	tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]						)
            lowercase														=	model(__lowerCAmelCase						)[0]
            # compare the actual values for a slice.
            lowercase														=	tf.constant(
                [
                    [
                        [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
                        [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
                        [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
                    ]
                ]						)
            self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,       expected_slice.numpy()		,       atol=1E-4						)						)




"""simple docstring"""






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      output_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		use_external_data_format=lowerCAmelCase__					,		enable_onnx_checker=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)
      else:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              False,
          )					,		output_path=output_path / """vae_decoder""" / """model.onnx"""					,		ordered_input_names=["""latent_sample""", """return_dict"""]					,		output_names=["""sample"""]					,		dynamic_axes={
              """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
          }					,		opset=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =argparse.ArgumentParser()

       parser.add_argument(
           """--model_path""",
           type=str,
           required=True,
           help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
       )

       parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
       parser.add_argument(
           """--opset""",
           default=1_4,
           type=int,
           help="""The version of the ONNX operator set to use.""",
       )
       parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

       __lowerCAmelCase    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



