



'''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   :      Optional[int]				,    lowerCAmelCase__   :      Optional[Any]				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			parent
													SCREAMING_SNAKE_CASE					:      List[str]     					=			13
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			7
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			True
													SCREAMING_SNAKE_CASE					:      int     					=			True
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			True
													SCREAMING_SNAKE_CASE					:      Dict     					=			99
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			32
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			2
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			4
													SCREAMING_SNAKE_CASE					:      str     					=			37
													SCREAMING_SNAKE_CASE					:      Dict     					=			'''gelu'''
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0.1
													SCREAMING_SNAKE_CASE					:      Dict     					=			0.1
													SCREAMING_SNAKE_CASE					:      Tuple     					=			5_12
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			16
													SCREAMING_SNAKE_CASE					:      Dict     					=			2
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0.02
													SCREAMING_SNAKE_CASE					:      Dict     					=			3
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			4
													SCREAMING_SNAKE_CASE					:      Dict     					=			None







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if self.use_input_mask:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]				)

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

													SCREAMING_SNAKE_CASE					:      str     					=			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   :      int				):

													"""simple docstring"""
													(
													    (
													    SCREAMING_SNAKE_CASE			
													)					,(
													    SCREAMING_SNAKE_CASE			
													)					,(
													    SCREAMING_SNAKE_CASE			
													)					,(
													    SCREAMING_SNAKE_CASE			
													)					,(
													    SCREAMING_SNAKE_CASE			
													)					,(
													    SCREAMING_SNAKE_CASE			
													)					,			
													)					:      Dict     					=			self.prepare_config_and_inputs()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			True
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
													SCREAMING_SNAKE_CASE					:      int     					=			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   :      int				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			TFEsmModel(config=_lowerCAmelCase				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													SCREAMING_SNAKE_CASE					:      Any     					=			model(_lowerCAmelCase				)

													SCREAMING_SNAKE_CASE					:      Any     					=			[input_ids, input_mask]
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(_lowerCAmelCase				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model(_lowerCAmelCase				)

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







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Union[str, Any]				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			True

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			TFEsmModel(config=_lowerCAmelCase				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			{
													    '''input_ids''': input_ids,
													    '''attention_mask''': input_mask,
													    '''encoder_hidden_states''': encoder_hidden_states,
													    '''encoder_attention_mask''': encoder_attention_mask,
													}
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(_lowerCAmelCase				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			[input_ids, input_mask]
													SCREAMING_SNAKE_CASE					:      Any     					=			model(_lowerCAmelCase				,    encoder_hidden_states=_lowerCAmelCase				)

													# Also check the case where encoder outputs are not passed
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				)

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







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			TFEsmForMaskedLM(config=_lowerCAmelCase				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model([input_ids, input_mask]				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.num_labels
													SCREAMING_SNAKE_CASE					:      List[str]     					=			TFEsmForTokenClassification(config=_lowerCAmelCase				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(_lowerCAmelCase				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.num_labels)				)







						def 					__lowercase    (  self   :      Tuple				):

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


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







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			TFEsmModelTester(self				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37				)







						def 					__lowercase    (  self   :      str				):

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







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_lowerCAmelCase				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs_for_decoder()
													self.model_tester.create_and_check_model_as_decoder(*_lowerCAmelCase				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase				)







						@slow
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				SCREAMING_SNAKE_CASE					:      str     					=			TFEsmModel.from_pretrained(_lowerCAmelCase				)
																				self.assertIsNotNone(_lowerCAmelCase				)







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

													"""simple docstring"""
													pass







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

													"""simple docstring"""
													pass







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			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
																											SCREAMING_SNAKE_CASE					:      int     					=			model.get_bias()
																											assert isinstance(_lowerCAmelCase				,    _lowerCAmelCase				)
																											for k, v in name.items():
																																		assert isinstance(_lowerCAmelCase				,    tf.Variable				)
																				else:
																											SCREAMING_SNAKE_CASE					:      Dict     					=			model.get_output_embeddings()
																											assert x is None
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model.get_bias()
																											assert name is None


@require_tf
class 	lowerCamelCase_							(					unittest.TestCase			):







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''				)

													SCREAMING_SNAKE_CASE					:      int     					=			tf.constant([[0, 1, 2, 3, 4, 5]]				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(_lowerCAmelCase				)[0]
													SCREAMING_SNAKE_CASE					:      Any     					=			[1, 6, 33]
													self.assertEqual(list(output.numpy().shape				)				,    _lowerCAmelCase				)
													# compare the actual values for a slice.
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tf.constant(
													    [
													        [
													            [8.92_1518, -10.58_9814, -6.467_1307],
													            [-6.396_7156, -13.91_1377, -1.121_1915],
													            [-7.78_1247, -13.95_1557, -3.74_0592],
													        ]
													    ]				)
													self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,    expected_slice.numpy()				,    atol=1e-2				)				)







						@slow
						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(_lowerCAmelCase				)[0]
													# compare the actual values for a slice.
													SCREAMING_SNAKE_CASE					:      Dict     					=			tf.constant(
													    [
													        [
													            [0.1444_3092, 0.5412_5327, 0.324_7739],
													            [0.3034_0484, 0.0052_6676, 0.3107_7722],
													            [0.3227_8043, -0.2498_7096, 0.341_4628],
													        ]
													    ]				)
													self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,    expected_slice.numpy()				,    atol=1e-4				)				)









'''simple docstring'''



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

							os.makedirs(F"""{class_data_dir}/images"""					,		exist_ok=A				)
							if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
														return

							while True:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			client.query(text=A				)
														if len(A				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(factor * num_images				)
																					SCREAMING_SNAKE_CASE					:      str     					=			ClipClient(
																					    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1					,		)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							with open(F"""{class_data_dir}/caption.txt"""					,		'''w'''				) as fa, open(F"""{class_data_dir}/urls.txt"""					,		'''w'''				) as fa, open(
							    F"""{class_data_dir}/images.txt"""					,		'''w'''				) as fa:
														while total < num_class_images:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			Image.open(BytesIO(img.content				)				)
																																			with open(F"""{class_data_dir}/images/{total}.jpg"""					,		'''wb'''				) as f:
																																										f.write(img.content				)
																																			fa.write(images['''caption'''] + '''\n'''				)
																																			fa.write(images['''url'''] + '''\n'''				)
																																			fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n'''				)
																																			total += 1
																																			pbar.update(1				)
																												else:
																																			continue
																					except Exception:
																												continue
							return
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




