




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 			__lowerCamelCase	:




    """simple docstring"""

    snake_case__ =				BlenderbotConfig
    snake_case__ =				{}
    snake_case__ =				"gelu"


    def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict=13       ,     SCREAMING_SNAKE_CASE__					:					Tuple=7       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=99       ,     SCREAMING_SNAKE_CASE__					:					Any=32       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					List[str]=4       ,     SCREAMING_SNAKE_CASE__					:					Tuple=37       ,     SCREAMING_SNAKE_CASE__					:					Tuple=0.1       ,     SCREAMING_SNAKE_CASE__					:					Any=0.1       ,     SCREAMING_SNAKE_CASE__					:					str=20       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					Any=1       ,     SCREAMING_SNAKE_CASE__					:					int=0       ,     )				->	Any:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			seq_length
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			use_labels
         lowerCAmelCase__          =			vocab_size
         lowerCAmelCase__          =			hidden_size
         lowerCAmelCase__          =			num_hidden_layers
         lowerCAmelCase__          =			num_attention_heads
         lowerCAmelCase__          =			intermediate_size

         lowerCAmelCase__          =			hidden_dropout_prob
         lowerCAmelCase__          =			attention_probs_dropout_prob
         lowerCAmelCase__          =			max_position_embeddings
         lowerCAmelCase__          =			eos_token_id
         lowerCAmelCase__          =			pad_token_id
         lowerCAmelCase__          =			bos_token_id


    def        a				(	self					:					Optional[int]       )				->	List[str]:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length - 1]       ,     self.vocab_size       )
         lowerCAmelCase__          =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )       ,     1       )
         lowerCAmelCase__          =			tf.concat([input_ids, eos_tensor]       ,     axis=1       )

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

         lowerCAmelCase__          =			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       ,     )
         lowerCAmelCase__          =			prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         return config, inputs_dict


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[int]:
         lowerCAmelCase__          =			TFBlenderbotModel(config=SCREAMING_SNAKE_CASE__       ).get_decoder()
         lowerCAmelCase__          =			inputs_dict["input_ids"]

         lowerCAmelCase__          =			input_ids[:1, :]
         lowerCAmelCase__          =			inputs_dict["attention_mask"][:1, :]
         lowerCAmelCase__          =			inputs_dict["head_mask"]
         lowerCAmelCase__          =			1

         # first forward pass
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     head_mask=SCREAMING_SNAKE_CASE__       ,     use_cache=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__     ,		lowerCAmelCase__          =			outputs.to_tuple()

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

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

         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       )[0]
         lowerCAmelCase__          =			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
         lowerCAmelCase__          =			int(ids_tensor((1,)       ,     output_from_past.shape[-1]       )       )
         lowerCAmelCase__          =			output_from_no_past[:, -3:, random_slice_idx]
         lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Dict=None       ,    lowerCAmelCase_					: int=None       ,    lowerCAmelCase_					: List[Any]=None       ,    lowerCAmelCase_					: Optional[Any]=None       ,    lowerCAmelCase_					: Tuple=None       ,    ):
     """simple docstring"""

     if attention_mask is None:
          lowerCAmelCase__          =			tf.cast(tf.math.not_equal(lowerCAmelCase_       ,    config.pad_token_id      )       ,    tf.inta      )
     if decoder_attention_mask is None:
          lowerCAmelCase__          =			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:
          lowerCAmelCase__          =			tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          lowerCAmelCase__          =			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          lowerCAmelCase__          =			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 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
    snake_case__ =				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
    snake_case__ =				(
        {
            "conversational": TFBlenderbotForConditionalGeneration,
            "feature-extraction": TFBlenderbotModel,
            "summarization": TFBlenderbotForConditionalGeneration,
            "text2text-generation": TFBlenderbotForConditionalGeneration,
            "translation": TFBlenderbotForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    snake_case__ =				True
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					int       )				->	Any:
         lowerCAmelCase__          =			TFBlenderbotModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       )				->	List[Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					List[Any]       )				->	Any:
         lowerCAmelCase__          =			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 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				["My friends are cool but they eat too many carbs."]
    snake_case__ =				"facebook/blenderbot-400M-distill"


    @cached_property
    def        a				(	self					:					Any       )				->	List[Any]:
         return BlenderbotTokenizer.from_pretrained(self.model_name       )


    @cached_property
    def        a				(	self					:					Tuple       )				->	int:
         lowerCAmelCase__          =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
         return model


    @slow
    def        a				(	self					:					Tuple       )				->	Tuple:
         lowerCAmelCase__          =			self.tokenizer(self.src_text       ,     return_tensors="tf"       )
         lowerCAmelCase__          =			self.model.generate(
             model_inputs.input_ids       ,     )
         lowerCAmelCase__          =			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?"
         )






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    **lowerCAmelCase_					: str      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoConfig.from_pretrained(lowerCAmelCase_       ,    **lowerCAmelCase_      )
     lowerCAmelCase__          =			AutoModelForSeqaSeqLM.from_config(lowerCAmelCase_      )
     model.save_pretrained(lowerCAmelCase_      )
     AutoTokenizer.from_pretrained(lowerCAmelCase_      ).save_pretrained(lowerCAmelCase_      )
     return model


if __name__ == "__main__":
     fire.Fire(save_randomly_initialized_version)
