
import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			SwinaSRConfig()

				if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								lowerCamelCase_						=			4
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								lowerCamelCase_						=			4
								lowerCamelCase_						=			4_8
								lowerCamelCase_						=			"pixelshuffle_aux"
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								lowerCamelCase_						=			[6, 6, 6, 6]
								lowerCamelCase_						=			6_0
								lowerCamelCase_						=			[6, 6, 6, 6]
								lowerCamelCase_						=			"pixelshuffledirect"
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								lowerCamelCase_						=			4
								lowerCamelCase_						=			"nearest+conv"
				elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
								lowerCamelCase_						=			1
								lowerCamelCase_						=			1
								lowerCamelCase_						=			1_2_6
								lowerCamelCase_						=			7
								lowerCamelCase_						=			2_55.0
								lowerCamelCase_						=			""

				return config





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				if "patch_embed.proj" in name and "layers" not in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "embeddings.patch_embeddings.projection"    )
				if "patch_embed.norm" in name:
								lowerCamelCase_						=			name.replace("patch_embed.norm"	, "embeddings.patch_embeddings.layernorm"    )
				if "layers" in name:
								lowerCamelCase_						=			name.replace("layers"	, "encoder.stages"    )
				if "residual_group.blocks" in name:
								lowerCamelCase_						=			name.replace("residual_group.blocks"	, "layers"    )
				if "attn.proj" in name:
								lowerCamelCase_						=			name.replace("attn.proj"	, "attention.output.dense"    )
				if "attn" in name:
								lowerCamelCase_						=			name.replace("attn"	, "attention.self"    )
				if "norm1" in name:
								lowerCamelCase_						=			name.replace("norm1"	, "layernorm_before"    )
				if "norm2" in name:
								lowerCamelCase_						=			name.replace("norm2"	, "layernorm_after"    )
				if "mlp.fc1" in name:
								lowerCamelCase_						=			name.replace("mlp.fc1"	, "intermediate.dense"    )
				if "mlp.fc2" in name:
								lowerCamelCase_						=			name.replace("mlp.fc2"	, "output.dense"    )
				if "q_bias" in name:
								lowerCamelCase_						=			name.replace("q_bias"	, "query.bias"    )
				if "k_bias" in name:
								lowerCamelCase_						=			name.replace("k_bias"	, "key.bias"    )
				if "v_bias" in name:
								lowerCamelCase_						=			name.replace("v_bias"	, "value.bias"    )
				if "cpb_mlp" in name:
								lowerCamelCase_						=			name.replace("cpb_mlp"	, "continuous_position_bias_mlp"    )
				if "patch_embed.proj" in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "patch_embed.projection"    )

				if name == "norm.weight":
								lowerCamelCase_						=			"layernorm.weight"
				if name == "norm.bias":
								lowerCamelCase_						=			"layernorm.bias"

				if "conv_first" in name:
								lowerCamelCase_						=			name.replace("conv_first"	, "first_convolution"    )

				if (
				    "upsample" in name
				    or "conv_before_upsample" in name
				    or "conv_bicubic" in name
				    or "conv_up" in name
				    or "conv_hr" in name
				    or "conv_last" in name
				    or "aux" in name
				):
								# heads
								if "conv_last" in name:
												lowerCamelCase_						=			name.replace("conv_last"	, "final_convolution"    )
								if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
												if "conv_before_upsample.0" in name:
																lowerCamelCase_						=			name.replace("conv_before_upsample.0"	, "conv_before_upsample"    )
												if "upsample.0" in name:
																lowerCamelCase_						=			name.replace("upsample.0"	, "upsample.convolution_0"    )
												if "upsample.2" in name:
																lowerCamelCase_						=			name.replace("upsample.2"	, "upsample.convolution_1"    )
												lowerCamelCase_						=			"upsample." + name
								elif config.upsampler == "pixelshuffledirect":
												lowerCamelCase_						=			name.replace("upsample.0.weight"	, "upsample.conv.weight"    )
												lowerCamelCase_						=			name.replace("upsample.0.bias"	, "upsample.conv.bias"    )
								else:
												pass
				else:
								lowerCamelCase_						=			"swin2sr." + name

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

								if "qkv" in key:
												lowerCamelCase_						=			key.split("."    )
												lowerCamelCase_						=			int(key_split[1]    )
												lowerCamelCase_						=			int(key_split[4]    )
												lowerCamelCase_						=			config.embed_dim

												if "weight" in key:
																lowerCamelCase_						=			val[:dim, :]
																lowerCamelCase_						=			val[dim : dim * 2, :]
																lowerCamelCase_						=			val[-dim:, :]
												else:
																lowerCamelCase_						=			val[:dim]
																lowerCamelCase_						=			val[dim : dim * 2]
																lowerCamelCase_						=			val[-dim:]
												pass
								else:
												lowerCamelCase_						=			val

				return orig_state_dict





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			get_config(lowerCamelCase__    )
				lowerCamelCase_						=			SwinaSRForImageSuperResolution(lowerCamelCase__    )
				model.eval()

				lowerCamelCase_						=			torch.hub.load_state_dict_from_url(lowerCamelCase__	, map_location="cpu"    )
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__    )

				if len(lowerCamelCase__    ) > 0:
								raise ValueError("Missing keys when converting: {}".format(lowerCamelCase__    )    )
				for key in unexpected_keys:
								if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
												raise ValueError(F'Unexpected key {key} in state_dict'    )

    # verify values
				lowerCamelCase_						=			"https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
				lowerCamelCase_						=			Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__    ).raw    ).convert("RGB"    )
				lowerCamelCase_						=			SwinaSRImageProcessor()
				# pixel_values = processor(image, return_tensors="pt").pixel_values

				lowerCamelCase_						=			1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
				lowerCamelCase_						=			Compose(
				    [
				        Resize((image_size, image_size)    ),
				        ToTensor(),
				        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]	, std=[0.2_29, 0.2_24, 0.2_25]    ),
				    ]    )
				lowerCamelCase_						=			transforms(lowerCamelCase__    ).unsqueeze(0    )

				if config.num_channels == 1:
								lowerCamelCase_						=			pixel_values[:, 0, :, :].unsqueeze(1    )

				lowerCamelCase_						=			model(lowerCamelCase__    )

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								lowerCamelCase_						=			torch.Size([1, 3, 5_1_2, 5_1_2]    )
								lowerCamelCase_						=			torch.tensor(
								    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]    )
				elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
								lowerCamelCase_						=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
								lowerCamelCase_						=			torch.tensor(
								    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]    )
				elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
								# TODO values didn't match exactly here
								lowerCamelCase_						=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
								lowerCamelCase_						=			torch.tensor(
								    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]    )
				elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
								lowerCamelCase_						=			torch.Size([1, 3, 5_1_2, 5_1_2]    )
								lowerCamelCase_						=			torch.tensor(
								    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]    )
				elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
								lowerCamelCase_						=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
								lowerCamelCase_						=			torch.tensor(
								    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]    )

				assert (
				    outputs.reconstruction.shape == expected_shape
				), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
				assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]	, lowerCamelCase__	, atol=1e-3    )
				print("Looks ok!"    )

				lowerCamelCase_						=			{
				    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
				        "swin2SR-classical-sr-x2-64"
				    ),
				    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
				        "swin2SR-classical-sr-x4-64"
				    ),
				    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
				        "swin2SR-compressed-sr-x4-48"
				    ),
				    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
				        "swin2SR-lightweight-x2-64"
				    ),
				    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
				        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
				    ),
				}
				lowerCamelCase_						=			url_to_name[checkpoint_url]

				if pytorch_dump_folder_path is not None:
								print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
								model.save_pretrained(lowerCamelCase__    )
								print(F'Saving image processor to {pytorch_dump_folder_path}'    )
								processor.save_pretrained(lowerCamelCase__    )

				if push_to_hub:
								model.push_to_hub(F'caidas/{model_name}'    )
								processor.push_to_hub(F'caidas/{model_name}'    )


if __name__ == "__main__":
			__A				=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
			    type=str,
			    help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

			__A				=parser.parse_args()
			convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




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     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  )				->       Optional[int]:
					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     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					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     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					(
					    (
					    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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			TFEsmModel(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )

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

					lowerCamelCase_						=			model(lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Tuple:
					lowerCamelCase_						=			True

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

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

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

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Optional[int]:
					lowerCamelCase_						=			TFEsmForMaskedLM(config=lowercase )
					lowerCamelCase_						=			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						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					lowerCamelCase_						=			self.num_labels
					lowerCamelCase_						=			TFEsmForTokenClassification(config=lowercase )
					lowerCamelCase_						=			{"input_ids": input_ids, "attention_mask": input_mask}
					lowerCamelCase_						=			model(lowercase )
					self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					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     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (
	    (
	        TFEsmModel,
	        TFEsmForMaskedLM,
	        TFEsmForSequenceClassification,
	        TFEsmForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase__         = (
	    {
	        'feature-extraction': TFEsmModel,
	        'fill-mask': TFEsmForMaskedLM,
	        'text-classification': TFEsmForSequenceClassification,
	        'token-classification': TFEsmForTokenClassification,
	        'zero-shot': TFEsmForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			TFEsmModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  hidden_size=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*lowercase )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			TFEsmModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )



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



	@unittest.skip("Protein models do not support embedding resizing." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					pass



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

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




@require_tf
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )

					lowerCamelCase_						=			tf.constant([[0, 1, 2, 3, 4, 5]] )
					lowerCamelCase_						=			model(lowercase )[0]
					lowerCamelCase_						=			[1, 6, 33]
					self.assertEqual(list(output.numpy().shape )						,  lowercase )
					# 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     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					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(lowercase )[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 ) )


