

'''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		,					UpperCamelCase__		,					)    ->   Dict:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   13
           A_						       =   7
           A_						       =   True
           A_						       =   True
           A_						       =   True
           A_						       =   99
           A_						       =   32
           A_						       =   2
           A_						       =   4
           A_						       =   37
           A_						       =   """gelu"""
           A_						       =   0.1
           A_						       =   0.1
           A_						       =   512
           A_						       =   16
           A_						       =   2
           A_						       =   0.02
           A_						       =   3
           A_						       =   4
           A_						       =   None



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

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

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

           A_						       =   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        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           (
               (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      
           )						       =   self.prepare_config_and_inputs()

           A_						       =   True
           A_						       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
           A_						       =   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        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''
           A_						       =   TFEsmModel(config=UpperCamelCase__							)
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           A_						       =   model(UpperCamelCase__							)

           A_						       =   [input_ids, input_mask]
           A_						       =   model(UpperCamelCase__							)

           A_						       =   model(UpperCamelCase__							)

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



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   True

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

           A_						       =   [input_ids, input_mask]
           A_						       =   model(UpperCamelCase__		,					encoder_hidden_states=UpperCamelCase__							)

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

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



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   str:





           '''simple docstring'''
           A_						       =   TFEsmForMaskedLM(config=UpperCamelCase__							)
           A_						       =   model([input_ids, input_mask]							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.num_labels
           A_						       =   TFEsmForTokenClassification(config=UpperCamelCase__							)
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           A_						       =   model(UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)							)







    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           (
               (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      (
               A_
           )       ,      
           )						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_tf
class        A__   (   _snake_case    ,    _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(
        (
            TFEsmModel,
            TFEsmForMaskedLM,
            TFEsmForSequenceClassification,
            TFEsmForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowercase						       =		(
        {
            "feature-extraction": TFEsmModel,
            "fill-mask": TFEsmForMaskedLM,
            "text-classification": TFEsmForSequenceClassification,
            "token-classification": TFEsmForTokenClassification,
            "zero-shot": TFEsmForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    lowercase						       =		False
    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   TFEsmModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					hidden_size=37							)



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





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase__							)



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





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__							)



    @slow
    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   TFEsmModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)



    @unittest.skip("""Protein models do not support embedding resizing."""							)
    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass







    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A_						       =   model_class(UpperCamelCase__							)
                  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
                         A_						       =   model.get_bias()
                         assert isinstance(UpperCamelCase__		,					UpperCamelCase__							)
                         for k, v in name.items():
                                assert isinstance(UpperCamelCase__		,					tf.Variable							)
                  else:
                         A_						       =   model.get_output_embeddings()
                         assert x is None
                         A_						       =   model.get_bias()
                         assert name is None


@require_tf
class        A__   (   unittest.TestCase      ):



    @slow
    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""							)

           A_						       =   tf.constant([[0, 1, 2, 3, 4, 5]]							)
           A_						       =   model(UpperCamelCase__							)[0]
           A_						       =   [1, 6, 33]
           self.assertEqual(list(output.numpy().shape							)		,					UpperCamelCase__							)
           # compare the actual values for a slice.
           A_						       =   tf.constant(
               [
                   [
                       [8.921518, -10.589814, -6.4671307],
                       [-6.3967156, -13.911377, -1.1211915],
                       [-7.781247, -13.951557, -3.740592],
                   ]
               ]							)
           self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,					expected_slice.numpy()		,					atol=1e-2							)							)







    @slow
    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""							)

           A_						       =   tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]							)
           A_						       =   model(UpperCamelCase__							)[0]
           # compare the actual values for a slice.
           A_						       =   tf.constant(
               [
                   [
                       [0.14443092, 0.54125327, 0.3247739],
                       [0.30340484, 0.00526676, 0.31077722],
                       [0.32278043, -0.24987096, 0.3414628],
                   ]
               ]							)
           self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,					expected_slice.numpy()		,					atol=1e-4							)							)



'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
      import tensorflow as tf

      from transformers import TFBlipTextModel
      from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class        A__   :



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=12		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=32		,					UpperCamelCase__=2		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=0		,					UpperCamelCase__=None		,					)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_input_mask
           A_						       =   use_labels
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   projection_dim
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   intermediate_size
           A_						       =   dropout
           A_						       =   attention_dropout
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   scope
           A_						       =   bos_token_id



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

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

           if input_mask is not None:
                  A_						       =   input_mask.numpy()
                  A_       ,      A_						       =   input_mask.shape
                  A_						       =   np.random.randint(1		,					seq_length - 1		,					size=(batch_size,)							)
                  for batch_idx, start_index in enumerate(UpperCamelCase__							):
                         A_						       =   1
                         A_						       =   0

           A_						       =   self.get_config()

           return config, input_ids, tf.convert_to_tensor(UpperCamelCase__							)



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





           '''simple docstring'''
           return BlipTextConfig(
               vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					projection_dim=self.projection_dim		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					intermediate_size=self.intermediate_size		,					dropout=self.dropout		,					attention_dropout=self.attention_dropout		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					bos_token_id=self.bos_token_id		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   TFBlipTextModel(config=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					training=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					training=UpperCamelCase__							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)							)
           self.parent.assertEqual(result.pooler_output.shape		,					(self.batch_size, self.hidden_size)							)







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





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           A_       ,      A_       ,      A_						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_tf
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(TFBlipTextModel,) if is_tf_available() else ()
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



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





           '''simple docstring'''
           A_						       =   BlipTextModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					hidden_size=37							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



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





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



    @unittest.skip(reason="""Blip does not use inputs_embeds"""							)
    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           pass



    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   TFBlipTextModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__=True							)    ->   str:





           '''simple docstring'''
           super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__							)
