

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 			a					:


						def __init__(    self  ,   A_  ,   A_=13  ,   A_=7  ,   A_=False  ,   A_=True  ,   A_=False  ,   A_=True  ,   A_=33  ,   A_=32  ,   A_=5  ,   A_=4  ,   A_=37  ,   A_="gelu"  ,   A_=0.1  ,   A_=0.1  ,   A_=512  ,   A_=16  ,   A_=2  ,   A_=0.02  ,   A_=3  ,   A_=4  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      parent
										_UpperCAmelCase    :						Any							=      batch_size
										_UpperCAmelCase    :						Tuple							=      seq_length
										_UpperCAmelCase    :						Optional[int]							=      is_training
										_UpperCAmelCase    :						List[Any]							=      use_input_mask
										_UpperCAmelCase    :						Union[str, Any]							=      use_token_type_ids
										_UpperCAmelCase    :						Tuple							=      use_labels
										_UpperCAmelCase    :						int							=      vocab_size
										_UpperCAmelCase    :						Optional[int]							=      hidden_size
										_UpperCAmelCase    :						Union[str, Any]							=      num_hidden_layers
										_UpperCAmelCase    :						List[Any]							=      num_attention_heads
										_UpperCAmelCase    :						List[str]							=      intermediate_size
										_UpperCAmelCase    :						Dict							=      hidden_act
										_UpperCAmelCase    :						Any							=      hidden_dropout_prob
										_UpperCAmelCase    :						int							=      attention_probs_dropout_prob
										_UpperCAmelCase    :						List[str]							=      max_position_embeddings
										_UpperCAmelCase    :						List[str]							=      type_vocab_size
										_UpperCAmelCase    :						Union[str, Any]							=      type_sequence_label_size
										_UpperCAmelCase    :						Any							=      initializer_range
										_UpperCAmelCase    :						str							=      num_labels
										_UpperCAmelCase    :						str							=      num_choices
										_UpperCAmelCase    :						Tuple							=      scope


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						Any							=      None
										if self.use_input_mask:
														_UpperCAmelCase    :						List[str]							=      random_attention_mask([self.batch_size, self.seq_length]		)

										_UpperCAmelCase    :						Any							=      None
										_UpperCAmelCase    :						Optional[int]							=      None
										_UpperCAmelCase    :						Tuple							=      None
										if self.use_labels:
														_UpperCAmelCase    :						Union[str, Any]							=      ids_tensor([self.batch_size]  ,   self.type_sequence_label_size		)
														_UpperCAmelCase    :						Tuple							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.num_labels		)
														_UpperCAmelCase    :						Optional[int]							=      ids_tensor([self.batch_size]  ,   self.num_choices		)

										_UpperCAmelCase    :						Optional[Any]							=      self.get_config()

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      EsmModel(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Union[str, Any]							=      model(A_  ,   attention_mask=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      model(A_		)
										_UpperCAmelCase    :						Any							=      model(A_		)

										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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      EsmForMaskedLM(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Optional[int]							=      model(A_  ,   attention_mask=A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.vocab_size)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.num_labels
										_UpperCAmelCase    :						Union[str, Any]							=      EsmForTokenClassification(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Dict							=      model(A_  ,   attention_mask=A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.num_labels)		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







@require_torch
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     False

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      EsmModelTester(self		)
										_UpperCAmelCase    :						Tuple							=      ConfigTester(self  ,   config_class=A_  ,   hidden_size=37		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														_UpperCAmelCase    :						List[str]							=      type
														self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*A_		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Tuple							=      EsmModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.model_tester.prepare_config_and_inputs()[0]
										_UpperCAmelCase    :						int							=      EsmEmbeddings(config=A_		)

										_UpperCAmelCase    :						List[Any]							=      torch.as_tensor([[12, 31, 13, model.padding_idx]]		)
										_UpperCAmelCase    :						str							=      torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ]		)
										_UpperCAmelCase    :						str							=      create_position_ids_from_input_ids(A_  ,   model.padding_idx		)
										self.assertEqual(position_ids.shape  ,   expected_positions.shape		)
										self.assertTrue(torch.all(torch.eq(A_  ,   A_		)		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.model_tester.prepare_config_and_inputs()[0]
										_UpperCAmelCase    :						List[Any]							=      EsmEmbeddings(config=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      torch.empty(2  ,   4  ,   30		)
										_UpperCAmelCase    :						List[str]							=      [
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										_UpperCAmelCase    :						Optional[Any]							=      torch.as_tensor([expected_single_positions, expected_single_positions]		)
										_UpperCAmelCase    :						Tuple							=      embeddings.create_position_ids_from_inputs_embeds(A_		)
										self.assertEqual(position_ids.shape  ,   expected_positions.shape		)
										self.assertTrue(torch.all(torch.eq(A_  ,   A_		)		)		)


						@unittest.skip("Esm does not support embedding resizing"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@unittest.skip("Esm does not support embedding resizing"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass





						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass







@require_torch
class 			a					(		UpperCAmelCase			):


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										with torch.no_grad():
														_UpperCAmelCase    :						Optional[Any]							=      EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
														model.eval()
														_UpperCAmelCase    :						List[Any]							=      torch.tensor([[0, 1, 2, 3, 4, 5]]		)
														_UpperCAmelCase    :						List[str]							=      model(A_		)[0]

														_UpperCAmelCase    :						List[str]							=      33

														_UpperCAmelCase    :						Optional[int]							=      torch.Size((1, 6, vocab_size)		)
														self.assertEqual(output.shape  ,   A_		)

														_UpperCAmelCase    :						Tuple							=      torch.tensor(
														    [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]]		)
														self.assertTrue(torch.allclose(output[:, :3, :3]  ,   A_  ,   atol=1e-4		)		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										with torch.no_grad():
														_UpperCAmelCase    :						Any							=      EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"		)
														model.eval()

														_UpperCAmelCase    :						Any							=      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]		)
														_UpperCAmelCase    :						List[Any]							=      model(A_		)[0]
														# compare the actual values for a slice.
														_UpperCAmelCase    :						Dict							=      torch.tensor(
														    [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]]		)
														self.assertTrue(torch.allclose(output[:, :3, :3]  ,   A_  ,   atol=1e-4		)		)





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


