

'''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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Dict      ,lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				parent
   __lowercase													=				1_3
   __lowercase													=				7
   __lowercase													=				True
   __lowercase													=				True
   __lowercase													=				True
   __lowercase													=				9_9
   __lowercase													=				3_2
   __lowercase													=				2
   __lowercase													=				4
   __lowercase													=				3_7
   __lowercase													=				'''gelu'''
   __lowercase													=				0.1
   __lowercase													=				0.1
   __lowercase													=				5_1_2
   __lowercase													=				1_6
   __lowercase													=				2
   __lowercase													=				0.0_2
   __lowercase													=				3
   __lowercase													=				4
   __lowercase													=				None






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   (
       __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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]      ,lowercase__     :  Tuple      ,lowercase__     :  Union[str, Any]    ):
   __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       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  str      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict      ,lowercase__     :  int      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  str      ,):
   __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       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict      ,lowercase__     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  List[str]      ,lowercase__     :  Any    ):
   __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       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  List[str]    ):
   __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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       __lowercase					
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_tf
class     lowercase_			(_UpperCAmelCase       , _UpperCAmelCase       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						str           =    (
      (
          TFEsmModel,
          TFEsmForMaskedLM,
          TFEsmForSequenceClassification,
          TFEsmForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    (
      {
          'feature-extraction': TFEsmModel,
          'fill-mask': TFEsmForMaskedLM,
          'text-classification': TFEsmForSequenceClassification,
          'token-classification': TFEsmForTokenClassification,
          'zero-shot': TFEsmForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				TFEsmModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowerCamelCase_      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):




   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       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   pass






  @unittest.skip('''Protein models do not support embedding resizing.'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __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     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __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, 3_3]
   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       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''    )

   __lowercase													=				tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 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 hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				hashlib.mda(image.tobytes()   )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]    ):
   __lowercase													=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}      ,lowercase__    )
   import datasets

   __lowercase													=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''    )
   __lowercase													=				depth_estimator(
       [
           Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           # RGBA
           dataset[0]['''file'''],
           # LA
           dataset[1]['''file'''],
           # L
           dataset[2]['''file'''],
       ]    )
   self.assertEqual(
       [
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @slow
  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''Intel/dpt-large'''
   __lowercase													=				pipeline('''depth-estimation'''      ,model=lowercase__    )
   __lowercase													=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
   __lowercase													=				hashimage(outputs['''depth''']    )

   # This seems flaky.
   # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )      ,2_9.3_0_4    )
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )      ,2.6_6_2    )






  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   # This is highly irregular to have no small tests.
   self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

