





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


lowerCAmelCase__  :List[Any]             =							['''gpt2''']
lowerCAmelCase__  :Union[str, Any]             =							'''gpt2'''

if is_tf_available():

		class 		__a				(    tf.Module      ):
				def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




											"""simple docstring"""





											super().__init__()
											_UpperCAmelCase					=      tokenizer
											_UpperCAmelCase					=      AutoConfig.from_pretrained(snake_case__						)
											_UpperCAmelCase					=      TFGPTaLMHeadModel.from_config(snake_case__						)






				@tf.function(input_signature=(tf.TensorSpec((None,)      ,   tf.string      ,   name='text'						),)						)
				def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	int:




											"""simple docstring"""





											_UpperCAmelCase					=      self.tokenizer(snake_case__						)
											_UpperCAmelCase					=      tokenized["input_ids"].to_tensor()

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

											_UpperCAmelCase					=      self.model(input_ids=snake_case__      ,   attention_mask=snake_case__						)["logits"]

											return outputs

@require_tf
@require_keras_nlp
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									super().setUp()

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

									_UpperCAmelCase					=      [
									    "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",
									]
									_UpperCAmelCase					=      list(zip(self.test_sentences      ,   self.test_sentences[::-1]						)						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									for tokenizer, tf_tokenizer in zip(self.tokenizers      ,   self.tf_tokenizers						):
																for test_inputs in self.test_sentences:
																							_UpperCAmelCase					=      tokenizer([test_inputs]      ,   return_tensors='tf'						)
																							_UpperCAmelCase					=      tf_tokenizer([test_inputs]						)

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

																														self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape						)						)
																														self.assertTrue(tf.reduce_all(tf.cast(snake_case__      ,   tf.intaa						) == tf_outputs_values						)						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





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

																							for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





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




									"""simple docstring"""





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

																_UpperCAmelCase					=      tf_tokenizer.get_config()
																_UpperCAmelCase					=      TFGPTaTokenizer.from_config(snake_case__						)
																_UpperCAmelCase					=      model_from_config(snake_case__						)

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






		@slow
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





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

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

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

																							assert out_length == max_length




from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel

@require_tf
class 		__a				:
		_a     :  Dict           =     BlenderbotConfig
		_a     :  Dict           =     {}
		_a     :  Union[str, Any]           =     'gelu'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=20      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=0      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      intermediate_size

									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      eos_token_id
									_UpperCAmelCase					=      pad_token_id
									_UpperCAmelCase					=      bos_token_id
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size						)
									_UpperCAmelCase					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)      ,   1						)
									_UpperCAmelCase					=      tf.concat([input_ids, eos_tensor]      ,   axis=1						)

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

									_UpperCAmelCase					=      self.config_cls(
									    vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   encoder_layers=self.num_hidden_layers      ,   decoder_layers=self.num_hidden_layers      ,   encoder_attention_heads=self.num_attention_heads      ,   decoder_attention_heads=self.num_attention_heads      ,   encoder_ffn_dim=self.intermediate_size      ,   decoder_ffn_dim=self.intermediate_size      ,   dropout=self.hidden_dropout_prob      ,   attention_dropout=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   eos_token_ids=[2]      ,   bos_token_id=self.bos_token_id      ,   pad_token_id=self.pad_token_id      ,   decoder_start_token_id=self.pad_token_id      ,   **self.config_updates      ,   )
									_UpperCAmelCase					=      prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return config, inputs_dict






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFBlenderbotModel(config=_SCREAMING_SNAKE_CASE						).get_decoder()
									_UpperCAmelCase					=      inputs_dict['input_ids']

									_UpperCAmelCase					=      input_ids[:1, :]
									_UpperCAmelCase					=      inputs_dict['attention_mask'][:1, :]
									_UpperCAmelCase					=      inputs_dict['head_mask']
									_UpperCAmelCase					=      1

									# first forward pass
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   head_mask=_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase		,	_UpperCAmelCase					=      outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									_UpperCAmelCase					=      ids_tensor((self.batch_size, 3)      ,   config.vocab_size						)
									_UpperCAmelCase					=      tf.cast(ids_tensor((self.batch_size, 3)      ,   2						)      ,   tf.inta						)

									# append to next input_ids and
									_UpperCAmelCase					=      tf.concat([input_ids, next_tokens]      ,   axis=-1						)
									_UpperCAmelCase					=      tf.concat([attention_mask, next_attn_mask]      ,   axis=-1						)

									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)[0]
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   past_key_values=_SCREAMING_SNAKE_CASE						)[0]

									self.parent.assertEqual(next_tokens.shape[1]      ,   output_from_past.shape[1]						)

									# select random slice
									_UpperCAmelCase					=      int(ids_tensor((1,)      ,   output_from_past.shape[-1]						)						)
									_UpperCAmelCase					=      output_from_no_past[:, -3:, random_slice_idx]
									_UpperCAmelCase					=      output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   rtol=1e-3						)



def 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		Dict      ,  a__:		Any      ,  a__:		Any=None      ,  a__:		List[Any]=None      ,  a__:		Union[str, Any]=None      ,  a__:		Tuple=None      ,  a__:		Union[str, Any]=None      ,  )  ->		Any:


							'''simple docstring'''





							if attention_mask is None:
														_UpperCAmelCase					=      tf.cast(tf.math.not_equal(a__      ,  config.pad_token_id	)      ,  tf.inta	)
							if decoder_attention_mask is None:
														_UpperCAmelCase					=      tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape      ,  dtype=tf.inta	),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,  config.pad_token_id	)      ,  tf.inta	),
														    ]      ,  axis=-1      ,  )
							if head_mask is None:
														_UpperCAmelCase					=      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														_UpperCAmelCase					=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														_UpperCAmelCase					=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  List[Any]           =     (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		_a     :  List[str]           =     (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		_a     :  List[str]           =     (
		    {
		        'conversational': TFBlenderbotForConditionalGeneration,
		        'feature-extraction': TFBlenderbotModel,
		        'summarization': TFBlenderbotForConditionalGeneration,
		        'text2text-generation': TFBlenderbotForConditionalGeneration,
		        'translation': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		_a     :  Dict           =     True
		_a     :  int           =     False
		_a     :  Union[str, Any]           =     False
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFBlenderbotModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									self.config_tester.run_common_tests()






		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE						)



@require_tokenizers
@require_tf
class 		__a				(    unittest.TestCase      ):
		_a     :  int           =     ['My friends are cool but they eat too many carbs.']
		_a     :  List[Any]           =     'facebook/blenderbot-400M-distill'
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return BlenderbotTokenizer.from_pretrained(self.model_name						)
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model






		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.tokenizer(self.src_text      ,   return_tensors='tf'						)
									_UpperCAmelCase					=      self.model.generate(
									    model_inputs.input_ids      ,   )
									_UpperCAmelCase					=      self.tokenizer.batch_decode(generated_ids.numpy()      ,   skip_special_tokens=_SCREAMING_SNAKE_CASE						)[0]
									assert (
									    generated_words
									    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
									)
