
"""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							,						UpperCAmelCase							,						):
							lowerCamelCase_					=  parent
							lowerCamelCase_					=  13
							lowerCamelCase_					=  7
							lowerCamelCase_					=  True
							lowerCamelCase_					=  True
							lowerCamelCase_					=  True
							lowerCamelCase_					=  99
							lowerCamelCase_					=  32
							lowerCamelCase_					=  2
							lowerCamelCase_					=  4
							lowerCamelCase_					=  37
							lowerCamelCase_					=  '''gelu'''
							lowerCamelCase_					=  0.1
							lowerCamelCase_					=  0.1
							lowerCamelCase_					=  512
							lowerCamelCase_					=  16
							lowerCamelCase_					=  2
							lowerCamelCase_					=  0.0_2
							lowerCamelCase_					=  3
							lowerCamelCase_					=  4
							lowerCamelCase_					=  None




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size   )

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

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

							lowerCamelCase_					=  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   ):
							(
							    (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, 
							)					=  self.prepare_config_and_inputs()

							lowerCamelCase_					=  True
							lowerCamelCase_					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							lowerCamelCase_					=  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							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  TFEsmModel(config=UpperCAmelCase   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							lowerCamelCase_					=  model(UpperCAmelCase   )

							lowerCamelCase_					=  [input_ids, input_mask]
							lowerCamelCase_					=  model(UpperCAmelCase   )

							lowerCamelCase_					=  model(UpperCAmelCase   )

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						):
							lowerCamelCase_					=  True

							lowerCamelCase_					=  TFEsmModel(config=UpperCAmelCase   )
							lowerCamelCase_					=  {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''encoder_hidden_states''': encoder_hidden_states,
							    '''encoder_attention_mask''': encoder_attention_mask,
							}
							lowerCamelCase_					=  model(UpperCAmelCase   )

							lowerCamelCase_					=  [input_ids, input_mask]
							lowerCamelCase_					=  model(UpperCAmelCase							,						encoder_hidden_states=UpperCAmelCase   )

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

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  TFEsmForMaskedLM(config=UpperCAmelCase   )
							lowerCamelCase_					=  model([input_ids, input_mask]   )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.num_labels
							lowerCamelCase_					=  TFEsmForTokenClassification(config=UpperCAmelCase   )
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							lowerCamelCase_					=  model(UpperCAmelCase   )
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, (
							    lowerCamelCase_
							)		, 
							)					=  config_and_inputs
							lowerCamelCase_					=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFEsmModelTester(self   )
							lowerCamelCase_					=  ConfigTester(self							,						config_class=UpperCAmelCase							,						hidden_size=37   )




				def        UpperCAmelCase__						(				self   ):
							self.config_tester.run_common_tests()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase   )




				@slow
				def        UpperCAmelCase__						(				self   ):
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase_					=  TFEsmModel.from_pretrained(UpperCAmelCase   )
										self.assertIsNotNone(UpperCAmelCase   )




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




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





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_		, lowerCamelCase_					=  self.model_tester.prepare_config_and_inputs_for_common()

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




@require_tf
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )

							lowerCamelCase_					=  tf.constant([[0, 1, 2, 3, 4, 5]]   )
							lowerCamelCase_					=  model(UpperCAmelCase   )[0]
							lowerCamelCase_					=  [1, 6, 33]
							self.assertEqual(list(output.numpy().shape   )							,						UpperCAmelCase   )
							# compare the actual values for a slice.
							lowerCamelCase_					=  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   ):
							lowerCamelCase_					=  TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''   )

							lowerCamelCase_					=  tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]   )
							lowerCamelCase_					=  model(UpperCAmelCase   )[0]
							# compare the actual values for a slice.
							lowerCamelCase_					=  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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor




@require_vision
class  __lowerCamelCase  ( unittest.TestCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  tempfile.mkdtemp()

							lowerCamelCase_					=  [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file''']   )
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8'''   ) as vocab_writer:
										vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )

							lowerCamelCase_					=  {
							    '''do_resize''': True,
							    '''size''': 20,
							    '''do_center_crop''': True,
							    '''crop_size''': 18,
							    '''do_normalize''': True,
							    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
							    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
							}
							lowerCamelCase_					=  os.path.join(self.tmpdirname							,						UpperCAmelCase   )
							with open(self.image_processor_file							,						'''w'''							,						encoding='''utf-8'''   ) as fp:
										json.dump(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizer.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return BertTokenizerFast.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							shutil.rmtree(self.tmpdirname   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta   )]
							lowerCamelCase_					=  [Image.fromarray(np.moveaxis(UpperCAmelCase							,						0							,						-1   )   ) for x in image_inputs]
							return image_inputs




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()
							lowerCamelCase_					=  self.get_rust_tokenizer()
							lowerCamelCase_					=  self.get_image_processor()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_slow.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname							,						use_fast=UpperCAmelCase   )

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )
							processor_fast.save_pretrained(self.tmpdirname   )
							lowerCamelCase_					=  AlignProcessor.from_pretrained(self.tmpdirname   )

							self.assertEqual(processor_slow.tokenizer.get_vocab()							,						tokenizer_slow.get_vocab()   )
							self.assertEqual(processor_fast.tokenizer.get_vocab()							,						tokenizer_fast.get_vocab()   )
							self.assertEqual(tokenizer_slow.get_vocab()							,						tokenizer_fast.get_vocab()   )
							self.assertIsInstance(processor_slow.tokenizer							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.tokenizer							,						UpperCAmelCase   )

							self.assertEqual(processor_slow.image_processor.to_json_string()							,						image_processor.to_json_string()   )
							self.assertEqual(processor_fast.image_processor.to_json_string()							,						image_processor.to_json_string()   )
							self.assertIsInstance(processor_slow.image_processor							,						UpperCAmelCase   )
							self.assertIsInstance(processor_fast.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  AlignProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor()   )
							processor.save_pretrained(self.tmpdirname   )

							lowerCamelCase_					=  self.get_tokenizer(bos_token='''(BOS)'''							,						eos_token='''(EOS)'''   )
							lowerCamelCase_					=  self.get_image_processor(do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							lowerCamelCase_					=  AlignProcessor.from_pretrained(
							    self.tmpdirname							,						bos_token='''(BOS)'''							,						eos_token='''(EOS)'''							,						do_normalize=UpperCAmelCase							,						padding_value=1.0   )

							self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()   )
							self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase   )

							self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string()   )
							self.assertIsInstance(processor.image_processor							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  image_processor(UpperCAmelCase							,						return_tensors='''np'''   )
							lowerCamelCase_					=  processor(images=UpperCAmelCase							,						return_tensors='''np'''   )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()							,						input_processor[key].sum()							,						delta=1e-2   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''

							lowerCamelCase_					=  processor(text=UpperCAmelCase   )

							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding='''max_length'''							,						max_length=64   )

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']   )

							# test if it raises when no input is passed
							with pytest.raises(UpperCAmelCase   ):
										processor()




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase_					=  processor.batch_decode(UpperCAmelCase   )
							lowerCamelCase_					=  tokenizer.batch_decode(UpperCAmelCase   )

							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_image_processor()
							lowerCamelCase_					=  self.get_tokenizer()

							lowerCamelCase_					=  AlignProcessor(tokenizer=UpperCAmelCase							,						image_processor=UpperCAmelCase   )

							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  self.prepare_image_inputs()

							lowerCamelCase_					=  processor(text=UpperCAmelCase							,						images=UpperCAmelCase   )

							self.assertListEqual(list(inputs.keys()   )							,						processor.model_input_names   )



