import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_A   					=    [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

_A   					=    [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

_A   					=    []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
						# loop over downblocks/upblocks

						for j in range(2):
												# loop over resnets/attentions for downblocks
												_A   					=    F'down_blocks.{i}.resnets.{j}.'
												_A   					=    F'input_blocks.{3*i + j + 1}.0.'
												unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

												if i < 3:
																		# no attention layers in down_blocks.3
																		_A   					=    F'down_blocks.{i}.attentions.{j}.'
																		_A   					=    F'input_blocks.{3*i + j + 1}.1.'
																		unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

						for j in range(3):
												# loop over resnets/attentions for upblocks
												_A   					=    F'up_blocks.{i}.resnets.{j}.'
												_A   					=    F'output_blocks.{3*i + j}.0.'
												unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

												if i > 0:
																		# no attention layers in up_blocks.0
																		_A   					=    F'up_blocks.{i}.attentions.{j}.'
																		_A   					=    F'output_blocks.{3*i + j}.1.'
																		unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

						if i < 3:
												# no downsample in down_blocks.3
												_A   					=    F'down_blocks.{i}.downsamplers.0.conv.'
												_A   					=    F'input_blocks.{3*(i+1)}.0.op.'
												unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

												# no upsample in up_blocks.3
												_A   					=    F'up_blocks.{i}.upsamplers.0.'
												_A   					=    F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_A   					=    'mid_block.attentions.0.'
_A   					=    'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
						_A   					=    F'mid_block.resnets.{j}.'
						_A   					=    F'middle_block.{2*j}.'
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				SCREAMING_SNAKE_CASE__		  =				{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								SCREAMING_SNAKE_CASE__		  =				sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																SCREAMING_SNAKE_CASE__		  =				v.replace(lowerCAmelCase__  ,					lowerCAmelCase__       )
												SCREAMING_SNAKE_CASE__		  =				v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												SCREAMING_SNAKE_CASE__		  =				v.replace(lowerCAmelCase__  ,					lowerCAmelCase__       )
								SCREAMING_SNAKE_CASE__		  =				v
				SCREAMING_SNAKE_CASE__		  =				{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

_A   					=    [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
						# down_blocks have two resnets
						for j in range(2):
												_A   					=    F'encoder.down_blocks.{i}.resnets.{j}.'
												_A   					=    F'encoder.down.{i}.block.{j}.'
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												_A   					=    F'down_blocks.{i}.downsamplers.0.'
												_A   					=    F'down.{i}.downsample.'
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

												_A   					=    F'up_blocks.{i}.upsamplers.0.'
												_A   					=    F'up.{3-i}.upsample.'
												vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

						# up_blocks have three resnets
						# also, up blocks in hf are numbered in reverse from sd
						for j in range(3):
												_A   					=    F'decoder.up_blocks.{i}.resnets.{j}.'
												_A   					=    F'decoder.up.{3-i}.block.{j}.'
												vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
						_A   					=    F'mid_block.resnets.{i}.'
						_A   					=    F'mid.block_{i+1}.'
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_A   					=    [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				return w.reshape(*w.shape  ,					1  ,					1       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				{k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												SCREAMING_SNAKE_CASE__		  =				v.replace(lowerCAmelCase__  ,					lowerCAmelCase__       )
								SCREAMING_SNAKE_CASE__		  =				v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																SCREAMING_SNAKE_CASE__		  =				v.replace(lowerCAmelCase__  ,					lowerCAmelCase__       )
												SCREAMING_SNAKE_CASE__		  =				v
				SCREAMING_SNAKE_CASE__		  =				{v: vae_state_dict[k] for k, v in mapping.items()}
				SCREAMING_SNAKE_CASE__		  =				['q', 'k', 'v', 'proj_out']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F"""mid.attn_1.{weight_name}.weight""" in k:
																print(F"""Reshaping {k} for SD format"""       )
																SCREAMING_SNAKE_CASE__		  =				reshape_weight_for_sd(lowerCAmelCase__       )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_A   					=    [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
_A   					=    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_A   					=    re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_A   					=    {'q': 0, 'k': 1, 'v': 2}




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				{}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith(".self_attn.q_proj.weight"       )
								    or k.endswith(".self_attn.k_proj.weight"       )
								    or k.endswith(".self_attn.v_proj.weight"       )
								):
												SCREAMING_SNAKE_CASE__		  =				k[: -len(".q_proj.weight"       )]
												SCREAMING_SNAKE_CASE__		  =				k[-len("q_proj.weight"       )]
												if k_pre not in capture_qkv_weight:
																SCREAMING_SNAKE_CASE__		  =				[None, None, None]
												SCREAMING_SNAKE_CASE__		  =				v
												continue

								if (
								    k.endswith(".self_attn.q_proj.bias"       )
								    or k.endswith(".self_attn.k_proj.bias"       )
								    or k.endswith(".self_attn.v_proj.bias"       )
								):
												SCREAMING_SNAKE_CASE__		  =				k[: -len(".q_proj.bias"       )]
												SCREAMING_SNAKE_CASE__		  =				k[-len("q_proj.bias"       )]
												if k_pre not in capture_qkv_bias:
																SCREAMING_SNAKE_CASE__		  =				[None, None, None]
												SCREAMING_SNAKE_CASE__		  =				v
												continue

								SCREAMING_SNAKE_CASE__		  =				textenc_pattern.sub(lambda __UpperCAmelCase       : protected[re.escape(m.group(0       )       )]  ,					lowerCAmelCase__       )
								SCREAMING_SNAKE_CASE__		  =				v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"       )
								SCREAMING_SNAKE_CASE__		  =				textenc_pattern.sub(lambda __UpperCAmelCase       : protected[re.escape(m.group(0       )       )]  ,					lowerCAmelCase__       )
								SCREAMING_SNAKE_CASE__		  =				torch.cat(lowerCAmelCase__       )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"       )
								SCREAMING_SNAKE_CASE__		  =				textenc_pattern.sub(lambda __UpperCAmelCase       : protected[re.escape(m.group(0       )       )]  ,					lowerCAmelCase__       )
								SCREAMING_SNAKE_CASE__		  =				torch.cat(lowerCAmelCase__       )

				return new_state_dict




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:
				return text_enc_dict


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()

						parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
						parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
						)

						_A   					=    parser.parse_args()

						assert args.model_path is not None, "Must provide a model path!"

						assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

						# Path for safetensors
						_A   					=    osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
						_A   					=    osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
						_A   					=    osp.join(args.model_path, 'text_encoder', 'model.safetensors')

						# Load models from safetensors if it exists, if it doesn't pytorch
						if osp.exists(unet_path):
												_A   					=    load_file(unet_path, device='cpu')
						else:
												_A   					=    osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
												_A   					=    torch.load(unet_path, map_location='cpu')

						if osp.exists(vae_path):
												_A   					=    load_file(vae_path, device='cpu')
						else:
												_A   					=    osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
												_A   					=    torch.load(vae_path, map_location='cpu')

						if osp.exists(text_enc_path):
												_A   					=    load_file(text_enc_path, device='cpu')
						else:
												_A   					=    osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
												_A   					=    torch.load(text_enc_path, map_location='cpu')

						# Convert the UNet model
						_A   					=    convert_unet_state_dict(unet_state_dict)
						_A   					=    {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

						# Convert the VAE model
						_A   					=    convert_vae_state_dict(vae_state_dict)
						_A   					=    {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

						# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
						_A   					=    'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

						if is_vaa_model:
												# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
												_A   					=    {'transformer.' + k: v for k, v in text_enc_dict.items()}
												_A   					=    convert_text_enc_state_dict_vaa(text_enc_dict)
												_A   					=    {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
						else:
												_A   					=    convert_text_enc_state_dict(text_enc_dict)
												_A   					=    {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

						# Put together new checkpoint
						_A   					=    {**unet_state_dict, **vae_state_dict, **text_enc_dict}
						if args.half:
												_A   					=    {k: v.half() for k, v in state_dict.items()}

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												_A   					=    {'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)





"""simple docstring"""






import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
						from transformers.models.esm.modeling_esm import (
						    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EsmEmbeddings,
						    create_position_ids_from_input_ids,
						)

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Optional[int]   , _snake_case			:				Dict=13   , _snake_case			:				Optional[Any]=7   , _snake_case			:				Union[str, Any]=False   , _snake_case			:				Any=True   , _snake_case			:				int=False   , _snake_case			:				int=True   , _snake_case			:				Tuple=33   , _snake_case			:				Optional[int]=32   , _snake_case			:				List[Any]=5   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				int=37   , _snake_case			:				Tuple="gelu"   , _snake_case			:				Dict=0.1   , _snake_case			:				Dict=0.1   , _snake_case			:				Tuple=512   , _snake_case			:				Any=16   , _snake_case			:				Union[str, Any]=2   , _snake_case			:				List[str]=0.02   , _snake_case			:				Optional[Any]=3   , _snake_case			:				int=4   , _snake_case			:				List[str]=None   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

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

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

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Any:
									return EsmConfig(
									    vocab_size=self.vocab_size   , hidden_size=self.hidden_size   , pad_token_id=1   , num_hidden_layers=self.num_hidden_layers   , 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   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				int   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				EsmModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)

									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 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict   , _snake_case			:				List[Any]   , _snake_case			:				Optional[int]   , _snake_case			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				EsmForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				List[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Tuple   , _snake_case			:				List[str]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				EsmForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       
									)		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		False

					a   	=		(
					    (
					        EsmForMaskedLM,
					        EsmModel,
					        EsmForSequenceClassification,
					        EsmForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		()
					a   	=		(
					    {
					        "feature-extraction": EsmModel,
					        "fill-mask": EsmForMaskedLM,
					        "text-classification": EsmForSequenceClassification,
					        "token-classification": EsmForTokenClassification,
					        "zero-shot": EsmForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				EsmModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				str		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE__		  =				type
													self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				EsmModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()[0]
									SCREAMING_SNAKE_CASE__		  =				EsmEmbeddings(config=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				torch.as_tensor([[12, 31, 13, model.padding_idx]]		)
									SCREAMING_SNAKE_CASE__		  =				torch.as_tensor(
									    [
									        [
									            0 + model.padding_idx + 1,
									            1 + model.padding_idx + 1,
									            2 + model.padding_idx + 1,
									            model.padding_idx,
									        ]
									    ]		)
									SCREAMING_SNAKE_CASE__		  =				create_position_ids_from_input_ids(_snake_case   , model.padding_idx		)
									self.assertEqual(position_ids.shape   , expected_positions.shape		)
									self.assertTrue(torch.all(torch.eq(_snake_case   , _snake_case		)		)		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()[0]
									SCREAMING_SNAKE_CASE__		  =				EsmEmbeddings(config=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				torch.empty(2   , 4   , 30		)
									SCREAMING_SNAKE_CASE__		  =				[
									    0 + embeddings.padding_idx + 1,
									    1 + embeddings.padding_idx + 1,
									    2 + embeddings.padding_idx + 1,
									    3 + embeddings.padding_idx + 1,
									]
									SCREAMING_SNAKE_CASE__		  =				torch.as_tensor([expected_single_positions, expected_single_positions]		)
									SCREAMING_SNAKE_CASE__		  =				embeddings.create_position_ids_from_inputs_embeds(_snake_case		)
									self.assertEqual(position_ids.shape   , expected_positions.shape		)
									self.assertTrue(torch.all(torch.eq(_snake_case   , _snake_case		)		)		)



					@unittest.skip("Esm does not support embedding resizing"		)
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       int:
									pass



					@unittest.skip("Esm does not support embedding resizing"		)
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Tuple:
									pass



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									pass



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       str:
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
													model.eval()
													SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 1, 2, 3, 4, 5]]		)
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]

													SCREAMING_SNAKE_CASE__		  =				33

													SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 6, vocab_size)		)
													self.assertEqual(output.shape   , _snake_case		)

													SCREAMING_SNAKE_CASE__		  =				torch.tensor(
													    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]		)
													self.assertTrue(torch.allclose(output[:, :3, :3]   , _snake_case   , atol=1e-4		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Any:
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
													model.eval()

													SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]		)
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]
													# compare the actual values for a slice.
													SCREAMING_SNAKE_CASE__		  =				torch.tensor(
													    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]		)
													self.assertTrue(torch.allclose(output[:, :3, :3]   , _snake_case   , atol=1e-4		)		)




