


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
   import tensorflow as tf

if is_tensorflow_text_available():
   from transformers.models.bert import TFBertTokenizer


_snake_case             =     ['''bert-base-uncased''', '''bert-base-cased''']
_snake_case             =     '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():
   class 	UpperCAmelCase_					(					tf.keras.Model    ):

      '''simple docstring'''




      def __init__(		self						,				__A ):






        """simple docstring"""





        super().__init__()
        lowerCamelCase :					str    =				tokenizer
        lowerCamelCase :					int    =				AutoConfig.from_pretrained(__A )
        lowerCamelCase :					Optional[Any]    =				TFAutoModel.from_config(__A )






      def _snake_case    (		self						,				__A ):






        """simple docstring"""





        lowerCamelCase :					Any    =				self.tokenizer(__A )
        lowerCamelCase :					Tuple    =				self.bert(**__A )
        return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     lowerCamelCase :					Optional[Any]    =				[
         BertTokenizer.from_pretrained(__A ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     lowerCamelCase :					List[str]    =				[TFBertTokenizer.from_pretrained(__A ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(__A						,				use_fast_bert_tokenizer=__A )
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers ) == len(self.tf_tokenizers )

     lowerCamelCase :					Any    =				[
         "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",
     ]
     lowerCamelCase :					str    =				list(zip(self.test_sentences						,				self.test_sentences[::-1] ) )






   def _snake_case    (		self ):






     """simple docstring"""





     for tokenizer, tf_tokenizer in zip(self.tokenizers						,				self.tf_tokenizers ):
       for test_inputs in (self.test_sentences, self.paired_sentences):
         lowerCamelCase :					List[str]    =				tokenizer(__A						,				return_tensors="tf"						,				padding="longest" )
         lowerCamelCase :					List[Any]    =				tf_tokenizer(__A )

         for key in python_outputs.keys():
           self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
           self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]						,				tf.intaa ) == tf_outputs[key] ) )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     for tf_tokenizer in self.tf_tokenizers:
       lowerCamelCase :					Tuple    =				tf_tokenizer(self.paired_sentences )
       lowerCamelCase :					Any    =				tf_tokenizer(
           text=[sentence[0] for sentence in self.paired_sentences]						,				text_pair=[sentence[1] for sentence in self.paired_sentences]						,				)
       for key in merged_outputs.keys():
         self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]						,				tf.intaa ) == separated_outputs[key] ) )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     for tf_tokenizer in self.tf_tokenizers:
       lowerCamelCase :					List[str]    =				tf.function(__A )
       for test_inputs in (self.test_sentences, self.paired_sentences):
         lowerCamelCase :					Optional[Any]    =				tf.constant(__A )
         lowerCamelCase :					Union[str, Any]    =				compiled_tokenizer(__A )
         lowerCamelCase :					List[Any]    =				tf_tokenizer(__A )

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






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     for tf_tokenizer in self.tf_tokenizers:
       lowerCamelCase :					int    =				ModelToSave(tokenizer=__A )
       lowerCamelCase :					str    =				tf.convert_to_tensor(self.test_sentences )
       lowerCamelCase :					Optional[Any]    =				model(__A )  # Build model with some sample inputs
       with TemporaryDirectory() as tempdir:
         lowerCamelCase :					Tuple    =				Path(__A ) / "saved.model"
         model.save(__A )
         lowerCamelCase :					Optional[Any]    =				tf.keras.models.load_model(__A )
       lowerCamelCase :					List[Any]    =				loaded_model(__A )
       # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
       self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) )						,				1e-5 )







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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

     super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				**__A )



