





'''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   	=    ["gpt2"]
a   	=    "gpt2"

if is_tf_available():







    class __a			( tf.Module      ):





       def __init__(       self					:      str					,lowerCamelCase					:      List[Any]     ):

          '''simple docstring'''

          super().__init__()
          __SCREAMING_SNAKE_CASE   				=							tokenizer
          __SCREAMING_SNAKE_CASE   				=							AutoConfig.from_pretrained(lowerCamelCase     )
          __SCREAMING_SNAKE_CASE   				=							TFGPTaLMHeadModel.from_config(lowerCamelCase     )





       @tf.function(input_signature=(tf.TensorSpec((None,)					,tf.string					,name="""text"""     ),)     )
       def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      str     ):

          '''simple docstring'''

          __SCREAMING_SNAKE_CASE   				=							self.tokenizer(lowerCamelCase     )
          __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=lowerCamelCase					,attention_mask=lowerCamelCase     )["""logits"""]

          return outputs







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





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      super().setUp()

      __SCREAMING_SNAKE_CASE   				=							[GPTaTokenizer.from_pretrained(lowerCamelCase     ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
      __SCREAMING_SNAKE_CASE   				=							[TFGPTaTokenizer.from_pretrained(lowerCamelCase     ) 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   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      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(lowerCamelCase					,tf.intaa     ) == tf_outputs_values     )     )





   @slow
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         __SCREAMING_SNAKE_CASE   				=							tf.function(lowerCamelCase     )
         for test_inputs in self.test_sentences:
            __SCREAMING_SNAKE_CASE   				=							tf.constant(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							compiled_tokenizer(lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							tf_tokenizer(lowerCamelCase     )

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





   @slow
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         __SCREAMING_SNAKE_CASE   				=							ModelToSave(tokenizer=lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							tf.convert_to_tensor([self.test_sentences[0]]     )
         __SCREAMING_SNAKE_CASE   				=							model.serving(lowerCamelCase     )  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
            __SCREAMING_SNAKE_CASE   				=							Path(lowerCamelCase     ) / """saved.model"""
            tf.saved_model.save(lowerCamelCase					,lowerCamelCase					,signatures={"""serving_default""": model.serving}     )
            __SCREAMING_SNAKE_CASE   				=							tf.saved_model.load(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							loaded_model.signatures["""serving_default"""](lowerCamelCase     )["""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   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         __SCREAMING_SNAKE_CASE   				=							tf.convert_to_tensor([self.test_sentences[0]]     )
         __SCREAMING_SNAKE_CASE   				=							tf_tokenizer(lowerCamelCase     )  # Build model with some sample inputs

         __SCREAMING_SNAKE_CASE   				=							tf_tokenizer.get_config()
         __SCREAMING_SNAKE_CASE   				=							TFGPTaTokenizer.from_config(lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							model_from_config(lowerCamelCase     )

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





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         __SCREAMING_SNAKE_CASE   				=							12_3123

         for max_length in [3, 5, 1024]:
            __SCREAMING_SNAKE_CASE   				=							tf.convert_to_tensor([self.test_sentences[0]]     )
            __SCREAMING_SNAKE_CASE   				=							tf_tokenizer(lowerCamelCase					,max_length=lowerCamelCase     )

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

            assert out_length == max_length












'''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 __a			( _snake_case      ):
   __UpperCamelCase :			str						   =						'esm'





   def __init__(       self					:      Tuple					,lowerCamelCase					:      List[Any]=None					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Any=None					,lowerCamelCase					:      Union[str, Any]=768					,lowerCamelCase					:      Tuple=12					,lowerCamelCase					:      int=12					,lowerCamelCase					:      Optional[int]=3072					,lowerCamelCase					:      List[Any]=0.1					,lowerCamelCase					:      Optional[int]=0.1					,lowerCamelCase					:      Any=1026					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      int=1E-1_2					,lowerCamelCase					:      Union[str, Any]="absolute"					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Optional[int]=False					,lowerCamelCase					:      int=False					,lowerCamelCase					:      Union[str, Any]=None					,lowerCamelCase					:      Any=None					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      super().__init__(pad_token_id=lowerCamelCase					,mask_token_id=lowerCamelCase					,**lowerCamelCase     )

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





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().to_dict()
      if isinstance(self.esmfold_config					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							self.esmfold_config.to_dict()
      return output







@dataclass
class __a			:
   __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   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      if self.trunk is None:
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig()
      elif isinstance(self.trunk					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig(**self.trunk     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.trunk.to_dict()
      return output







@dataclass
class __a			:
   __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   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      if self.structure_module is None:
         __SCREAMING_SNAKE_CASE   				=							StructureModuleConfig()
      elif isinstance(self.structure_module					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							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}."""     )

      __SCREAMING_SNAKE_CASE   				=							self.sequence_state_dim // self.sequence_head_width
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.structure_module.to_dict()
      return output







@dataclass
class __a			:
   __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   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return asdict(self     )





def     __magic_name__							(				)    ->       Dict:

   '''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>",
   )





