


'''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      lowerCamelCase_  :






      def __init__(   self							:       Any		,		_A							:       int		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							parent
          UpperCAmelCase__     :						str						=							13
          UpperCAmelCase__     :						int						=							7
          UpperCAmelCase__     :						Tuple						=							True
          UpperCAmelCase__     :						List[str]						=							True
          UpperCAmelCase__     :						Dict						=							True
          UpperCAmelCase__     :						Dict						=							99
          UpperCAmelCase__     :						List[Any]						=							32
          UpperCAmelCase__     :						List[str]						=							2
          UpperCAmelCase__     :						Any						=							4
          UpperCAmelCase__     :						str						=							37
          UpperCAmelCase__     :						Any						=							'''gelu'''
          UpperCAmelCase__     :						Optional[int]						=							0.1
          UpperCAmelCase__     :						Dict						=							0.1
          UpperCAmelCase__     :						Tuple						=							512
          UpperCAmelCase__     :						str						=							16
          UpperCAmelCase__     :						List[Any]						=							2
          UpperCAmelCase__     :						Union[str, Any]						=							0.0_2
          UpperCAmelCase__     :						Union[str, Any]						=							3
          UpperCAmelCase__     :						Any						=							4
          UpperCAmelCase__     :						Any						=							None






      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[str]						=							ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size					)

          UpperCAmelCase__     :						Optional[int]						=							None
          if self.use_input_mask:
              UpperCAmelCase__     :						Optional[int]						=							random_attention_mask([self.batch_size, self.seq_length]					)

          UpperCAmelCase__     :						Dict						=							None
          UpperCAmelCase__     :						int						=							None
          UpperCAmelCase__     :						Dict						=							None
          if self.use_labels:
              UpperCAmelCase__     :						Union[str, Any]						=							ids_tensor([self.batch_size]		,		self.type_sequence_label_size					)
              UpperCAmelCase__     :						List[str]						=							ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels					)
              UpperCAmelCase__     :						List[str]						=							ids_tensor([self.batch_size]		,		self.num_choices					)

          UpperCAmelCase__     :						Any						=							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 lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          (
              (
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		
          )     :						Dict						=							self.prepare_config_and_inputs()

          UpperCAmelCase__     :						List[str]						=							True
          UpperCAmelCase__     :						Optional[int]						=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
          UpperCAmelCase__     :						Dict						=							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 lowercase_				(   self							:       Optional[Any]		,		_A							:       Union[str, Any]		,		_A							:       Union[str, Any]		,		_A							:       int		,		_A							:       int		,		_A							:       Union[str, Any]		,		_A							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							TFEsmModel(config=_A					)
          UpperCAmelCase__     :						Union[str, Any]						=							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          UpperCAmelCase__     :						str						=							model(_A					)

          UpperCAmelCase__     :						str						=							[input_ids, input_mask]
          UpperCAmelCase__     :						List[Any]						=							model(_A					)

          UpperCAmelCase__     :						Optional[int]						=							model(_A					)

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






      def lowercase_				(   self							:       List[str]		,		_A							:       str		,		_A							:       Tuple		,		_A							:       Any		,		_A							:       Any		,		_A							:       Union[str, Any]		,		_A							:       int		,		_A							:       Optional[Any]		,		_A							:       str		,		):


          '''simple docstring'''


          UpperCAmelCase__     :						int						=							True

          UpperCAmelCase__     :						Any						=							TFEsmModel(config=_A					)
          UpperCAmelCase__     :						Tuple						=							{
              '''input_ids''': input_ids,
              '''attention_mask''': input_mask,
              '''encoder_hidden_states''': encoder_hidden_states,
              '''encoder_attention_mask''': encoder_attention_mask,
          }
          UpperCAmelCase__     :						List[str]						=							model(_A					)

          UpperCAmelCase__     :						Union[str, Any]						=							[input_ids, input_mask]
          UpperCAmelCase__     :						List[str]						=							model(_A		,		encoder_hidden_states=_A					)

          # Also check the case where encoder outputs are not passed
          UpperCAmelCase__     :						str						=							model(_A		,		attention_mask=_A					)

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






      def lowercase_				(   self							:       List[str]		,		_A							:       int		,		_A							:       Tuple		,		_A							:       List[Any]		,		_A							:       Dict		,		_A							:       List[Any]		,		_A							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							TFEsmForMaskedLM(config=_A					)
          UpperCAmelCase__     :						str						=							model([input_ids, input_mask]					)
          self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)					)






      def lowercase_				(   self							:       int		,		_A							:       List[str]		,		_A							:       List[Any]		,		_A							:       List[Any]		,		_A							:       str		,		_A							:       Union[str, Any]		,		_A							:       Tuple					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							self.num_labels
          UpperCAmelCase__     :						Any						=							TFEsmForTokenClassification(config=_A					)
          UpperCAmelCase__     :						Tuple						=							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          UpperCAmelCase__     :						List[Any]						=							model(_A					)
          self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)					)






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		(
              UpperCAmelCase__
          )		,		
          )     :						Union[str, Any]						=							config_and_inputs
          UpperCAmelCase__     :						Optional[int]						=							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




@require_tf
class      lowerCamelCase_  (      __a	,			__a	,			unittest.TestCase ):
      lowerCAmelCase__							=	(
          (
              TFEsmModel,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      lowerCAmelCase__							=	(
          {
              'feature-extraction': TFEsmModel,
              'fill-mask': TFEsmForMaskedLM,
              'text-classification': TFEsmForSequenceClassification,
              'token-classification': TFEsmForTokenClassification,
              'zero-shot': TFEsmForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      lowerCAmelCase__							=	False
      lowerCAmelCase__							=	False






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


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							TFEsmModelTester(self					)
          UpperCAmelCase__     :						Optional[Any]						=							ConfigTester(self		,		config_class=_A		,		hidden_size=37					)






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						str						=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_A					)






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


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[int]						=							self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*_A					)






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_A					)






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Any						=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_A					)






      @slow
      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase__     :						int						=							TFEsmModel.from_pretrained(_A					)
              self.assertIsNotNone(_A					)






      @unittest.skip('''Protein models do not support embedding resizing.'''					)
      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          pass






      @unittest.skip('''Protein models do not support embedding resizing.'''					)
      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          pass






      def lowercase_				(   self							:       Optional[Any]					):


          '''simple docstring'''


          UpperCAmelCase__		,		UpperCAmelCase__     :						Union[str, Any]						=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase__     :						Dict						=							model_class(_A					)
              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
                  UpperCAmelCase__     :						List[Any]						=							model.get_bias()
                  assert isinstance(_A		,		_A					)
                  for k, v in name.items():
                      assert isinstance(_A		,		tf.Variable					)
              else:
                  UpperCAmelCase__     :						Union[str, Any]						=							model.get_output_embeddings()
                  assert x is None
                  UpperCAmelCase__     :						Tuple						=							model.get_bias()
                  assert name is None




@require_tf
class      lowerCamelCase_  (      unittest.TestCase ):






      @slow
      def lowercase_				(   self							:       int					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)

          UpperCAmelCase__     :						Optional[Any]						=							tf.constant([[0, 1, 2, 3, 4, 5]]					)
          UpperCAmelCase__     :						Optional[Any]						=							model(_A					)[0]
          UpperCAmelCase__     :						List[Any]						=							[1, 6, 33]
          self.assertEqual(list(output.numpy().shape					)		,		_A					)
          # compare the actual values for a slice.
          UpperCAmelCase__     :						Dict						=							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 lowercase_				(   self							:       Union[str, Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)

          UpperCAmelCase__     :						List[Any]						=							tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
          UpperCAmelCase__     :						Any						=							model(_A					)[0]
          # compare the actual values for a slice.
          UpperCAmelCase__     :						Optional[int]						=							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					)					)






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
				import jax



@require_flax
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= FlaxAutoencoderKL





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						4
				SCREAMING_SNAKE_CASE_								=						3
				SCREAMING_SNAKE_CASE_								=						(32, 32)

				SCREAMING_SNAKE_CASE_								=						jax.random.PRNGKey(0      )
				SCREAMING_SNAKE_CASE_								=						jax.random.uniform(SCREAMING_SNAKE_CASE      ,     ((batch_size, num_channels) + sizes)      )

				return {"sample": image, "prng_key": prng_key}





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{
				    'block_out_channels': [32, 64],
				    'in_channels': 3,
				    'out_channels': 3,
				    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
				    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
				    'latent_channels': 4,
				}
				SCREAMING_SNAKE_CASE_								=						self.dummy_input
				return init_dict, inputs_dict
