





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


SCREAMING_SNAKE_CASE_						   =       ['gpt2']
SCREAMING_SNAKE_CASE_						   =       'gpt2'

if is_tf_available():

      class 		UpperCamelCase__ (      tf.Module   ):



           '''simple docstring'''

           def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    int       )     ->		Optional[Any]:




                 '''simple docstring'''


                 super().__init__()
                 SCREAMING_SNAKE_CASE		=						tokenizer
                 SCREAMING_SNAKE_CASE		=						AutoConfig.from_pretrained(A__       )
                 SCREAMING_SNAKE_CASE		=						TFGPTaLMHeadModel.from_config(A__       )






           @tf.function(input_signature=(tf.TensorSpec((None,)						,tf.string						,name="""text"""       ),)       )
           def 				SCREAMING_SNAKE_CASE__  (							self						:    str						,lowerCamelCase__						:    Dict       )     ->		int:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE		=						self.tokenizer(A__       )
                 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=A__						,attention_mask=A__       )["""logits"""]

                 return outputs

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



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Tuple:




           '''simple docstring'''


           super().setUp()

           SCREAMING_SNAKE_CASE		=						[GPTaTokenizer.from_pretrained(A__       ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
           SCREAMING_SNAKE_CASE		=						[TFGPTaTokenizer.from_pretrained(A__       ) 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						:    Optional[int]       )     ->		Union[str, Any]:




           '''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(A__						,tf.intaa       ) == tf_outputs_values       )       )






     @slow
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Optional[Any]:




           '''simple docstring'''


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

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






     @slow
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		int:




           '''simple docstring'''


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

                 SCREAMING_SNAKE_CASE		=						tf_tokenizer.get_config()
                 SCREAMING_SNAKE_CASE		=						TFGPTaTokenizer.from_config(A__       )
                 SCREAMING_SNAKE_CASE		=						model_from_config(A__       )

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






     @slow
     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Optional[int]:




           '''simple docstring'''


           for tf_tokenizer in self.tf_tokenizers:
                 # for the test to run
                 SCREAMING_SNAKE_CASE		=						123123

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

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

                       assert out_length == max_length





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		TaConfig(
						    vocab_size=A__    ,       d_model=A__    ,       num_heads=A__    ,       d_kv=A__    ,       d_ff=A__    ,       dropout_rate=A__    ,       feed_forward_proj=A__    ,       is_decoder=A__    ,       is_encoder_decoder=A__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

						A__							:    Optional[Any]        =		encoder_input_tokens.shape[1]
						A__							:    Optional[int]        =		torch.arange(A__    ,       device=encoder_input_tokens.device			)
						x += self.position_encoding(A__			)

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

						for lyr in self.encoders:
							A__							:    List[str]        =		lyr(A__    ,       A__			)[0]
						A__							:    List[Any]        =		self.layer_norm(A__			)

						return self.dropout_post(A__			), encoder_inputs_mask



