




"""simple docstring"""

from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

# TODO Update this
a							      =     {
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      '''esm'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				_UpperCAmelCase			:			List[Any]=None       ,				_UpperCAmelCase			:			int=768       ,				_UpperCAmelCase			:			Union[str, Any]=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			int=3_072       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Any=0.1       ,				_UpperCAmelCase			:			int=1_026       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			Optional[int]=1E-1_2       ,				_UpperCAmelCase			:			str="absolute"       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			Any=None       ,				_UpperCAmelCase			:			Optional[Any]=False       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			List[str]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				mask_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  emb_layer_norm_before
       _A     =  token_dropout
       _A     =  is_folding_model
       if is_folding_model:
             if esmfold_config is None:
                   logger.info('No esmfold_config supplied for folding model, using default values.'						)
                   _A     =  EsmFoldConfig()
             elif isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						):
                   _A     =  EsmFoldConfig(**_UpperCAmelCase						)
             _A     =  esmfold_config
             if vocab_list is None:
                   logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'						)
                   _A     =  get_default_vocab_list()
             else:
                   _A     =  vocab_list
       else:
             _A     =  None
             _A     =  None
       if self.esmfold_config is not None and getattr(self.esmfold_config       ,				'use_esm_attn_map'       ,				_UpperCAmelCase						):
             raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'						)



 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  super().to_dict()
       if isinstance(self.esmfold_config       ,				_UpperCAmelCase						):
             _A     =  self.esmfold_config.to_dict()
       return output





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	str     =      None
 UpperCAmelCase       :	bool     =      True
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	float     =      0

 UpperCAmelCase       :	bool     =      True
 UpperCAmelCase       :	bool     =      False

 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	"TrunkConfig"     =      None

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       if self.trunk is None:
             _A     =  TrunkConfig()
       elif isinstance(self.trunk       ,				_UpperCAmelCase						):
             _A     =  TrunkConfig(**self.trunk						)



 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  asdict(self						)
       _A     =  self.trunk.to_dict()
       return output





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	int     =      48
 UpperCAmelCase       :	int     =      1024
 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	float     =      0
 UpperCAmelCase       :	float     =      0
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	int     =      4
 UpperCAmelCase       :	Optional[int]     =      128
 UpperCAmelCase       :	"StructureModuleConfig"     =      None

 def      lowerCAmelCase_				(					self			:			str						):
       if self.structure_module is None:
             _A     =  StructureModuleConfig()
       elif isinstance(self.structure_module       ,				_UpperCAmelCase						):
             _A     =  StructureModuleConfig(**self.structure_module						)

       if self.max_recycles <= 0:
             raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.'''						)
       if self.sequence_state_dim % self.sequence_state_dim != 0:
             raise ValueError(
                 '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
                 F''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''						)
       if self.pairwise_state_dim % self.pairwise_state_dim != 0:
             raise ValueError(
                 '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
                 F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''						)

       _A     =  self.sequence_state_dim // self.sequence_head_width
       _A     =  self.pairwise_state_dim // self.pairwise_head_width

       if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
             raise ValueError(
                 '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
                 F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''						)
       if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
             raise ValueError(
                 '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
                 F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''						)
       if self.pairwise_state_dim % 2 != 0:
             raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''						)

       if self.dropout >= 0.4:
             raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.'''						)



 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  asdict(self						)
       _A     =  self.structure_module.to_dict()
       return output





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	int     =      384
 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	int     =      16
 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	int     =      12
 UpperCAmelCase       :	int     =      4
 UpperCAmelCase       :	int     =      8
 UpperCAmelCase       :	float     =      0.1
 UpperCAmelCase       :	int     =      8
 UpperCAmelCase       :	int     =      1
 UpperCAmelCase       :	int     =      2
 UpperCAmelCase       :	int     =      7
 UpperCAmelCase       :	int     =      10
 UpperCAmelCase       :	float     =      1E-8
 UpperCAmelCase       :	float     =      1E5

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       return asdict(self						)






def 		_snake_case	(   )       ->   List[Any]:






      '''simple docstring'''


      return (
          "<cls>",
          "<pad>",
          "<eos>",
          "<unk>",
          "L",
          "A",
          "G",
          "V",
          "S",
          "E",
          "R",
          "T",
          "I",
          "D",
          "P",
          "K",
          "Q",
          "N",
          "F",
          "Y",
          "M",
          "H",
          "W",
          "C",
          "X",
          "B",
          "U",
          "Z",
          "O",
          ".",
          "-",
          "<null_1>",
          "<mask>",
      )




"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						super().setUp()

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


