


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, lowerCamelCase, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						parent
           _lowercase			:				Union[str, Any] 						=						13
           _lowercase			:				Optional[Any] 						=						7
           _lowercase			:				Optional[Any] 						=						True
           _lowercase			:				int 						=						True
           _lowercase			:				List[Any] 						=						True
           _lowercase			:				int 						=						99
           _lowercase			:				List[Any] 						=						32
           _lowercase			:				Optional[int] 						=						2
           _lowercase			:				Tuple 						=						4
           _lowercase			:				Any 						=						37
           _lowercase			:				List[str] 						=						'gelu'
           _lowercase			:				List[str] 						=						0.1
           _lowercase			:				int 						=						0.1
           _lowercase			:				Union[str, Any] 						=						5_12
           _lowercase			:				Any 						=						16
           _lowercase			:				Any 						=						2
           _lowercase			:				List[Any] 						=						0.0_2
           _lowercase			:				List[Any] 						=						3
           _lowercase			:				Optional[int] 						=						4
           _lowercase			:				Union[str, Any] 						=						None
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

           _lowercase			:				List[Any] 						=						None
           if self.use_input_mask:
               _lowercase			:				Dict 						=						random_attention_mask([self.batch_size, self.seq_length])

           _lowercase			:				str 						=						None
           _lowercase			:				Dict 						=						None
           _lowercase			:				List[str] 						=						None
           if self.use_labels:
               _lowercase			:				List[str] 						=						ids_tensor([self.batch_size], self.type_sequence_label_size)
               _lowercase			:				Dict 						=						ids_tensor([self.batch_size, self.seq_length], self.num_labels)
               _lowercase			:				Tuple 						=						ids_tensor([self.batch_size], self.num_choices)

           _lowercase			:				Tuple 						=						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      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           (
               (
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							(
               _lowercase
           )						,							
           )			:				Union[str, Any] 						=						self.prepare_config_and_inputs()

           _lowercase			:				Optional[Any] 						=						True
           _lowercase			:				Optional[Any] 						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase			:				str 						=						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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Dict 						=						TFEsmModel(config=lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						{'input_ids': input_ids, 'attention_mask': input_mask}
           _lowercase			:				Tuple 						=						model(lowerCamelCase)

           _lowercase			:				List[Any] 						=						[input_ids, input_mask]
           _lowercase			:				Any 						=						model(lowerCamelCase)

           _lowercase			:				Optional[int] 						=						model(lowerCamelCase)

           self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, )			->  str:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						True

           _lowercase			:				int 						=						TFEsmModel(config=lowerCamelCase)
           _lowercase			:				Any 						=						{
               'input_ids': input_ids,
               'attention_mask': input_mask,
               'encoder_hidden_states': encoder_hidden_states,
               'encoder_attention_mask': encoder_attention_mask,
           }
           _lowercase			:				Optional[int] 						=						model(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						[input_ids, input_mask]
           _lowercase			:				Any 						=						model(lowerCamelCase, encoder_hidden_states=lowerCamelCase)

           # Also check the case where encoder outputs are not passed
           _lowercase			:				Optional[int] 						=						model(lowerCamelCase, attention_mask=lowerCamelCase)

           self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           _lowercase			:				Tuple 						=						TFEsmForMaskedLM(config=lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						model([input_ids, input_mask])
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

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




       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

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






@require_tf
class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      List[Any]       					=     (
           (
               TFEsmModel,
               TFEsmForMaskedLM,
               TFEsmForSequenceClassification,
               TFEsmForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       lowercase_					:      Any       					=     (
           {
               """feature-extraction""": TFEsmModel,
               """fill-mask""": TFEsmForMaskedLM,
               """text-classification""": TFEsmForSequenceClassification,
               """token-classification""": TFEsmForTokenClassification,
               """zero-shot""": TFEsmForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowercase_					:      Any       					=     False
       lowercase_					:      Optional[int]       					=     False
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Tuple 						=						TFEsmModelTester(self)
           _lowercase			:				List[Any] 						=						ConfigTester(self, config_class=lowerCamelCase, hidden_size=37)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				str 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*lowerCamelCase)
       @slow
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase			:				Any 						=						TFEsmModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)
       @unittest.skip('Protein models do not support embedding resizing.')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('Protein models do not support embedding resizing.')
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           pass




       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Dict 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				List[str] 						=						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			:				str 						=						model.get_bias()
                   assert isinstance(lowerCamelCase, lowerCamelCase)
                   for k, v in name.items():
                       assert isinstance(lowerCamelCase, tf.Variable)
               else:
                   _lowercase			:				List[str] 						=						model.get_output_embeddings()
                   assert x is None
                   _lowercase			:				List[str] 						=						model.get_bias()
                   assert name is None






@require_tf
class _lowerCamelCase(		unittest.TestCase  ):
       @slow
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				str 						=						TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D')

           _lowercase			:				List[Any] 						=						tf.constant([[0, 1, 2, 3, 4, 5]])
           _lowercase			:				List[Any] 						=						model(lowerCamelCase)[0]
           _lowercase			:				Dict 						=						[1, 6, 33]
           self.assertEqual(list(output.numpy().shape), lowerCamelCase)
           # compare the actual values for a slice.
           _lowercase			:				Any 						=						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      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Dict 						=						TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D')

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





"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__UpperCamelCase    : int				 =						input('''Enter numbers separated by a comma:\n''').strip()
							__UpperCamelCase    : Optional[Any]				 =						[int(item) for item in user_input.split(''',''')]
							print(F'''{cocktail_shaker_sort(unsorted) = }''')




