


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  return 1.0 / (1.0 + np.exp(-_outputs				))







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Any:
  snake_case__						= np.max(_outputs							,   axis=-1							,   keepdims=__lowerCAmelCase				)
  snake_case__						= np.exp(_outputs - maxes				)
  return shifted_exp / shifted_exp.sum(axis=-1							,   keepdims=__lowerCAmelCase				)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'sigmoid'
     __lowercase				:							int	   =       'softmax'
     __lowercase				:							Optional[Any]	   =       'none'




@add_end_docstrings(
    snake_case_   ,R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '   ,)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       False
     __lowercase				:							List[str]	   =       ClassificationFunction.NONE

     def __init__(					self:Optional[Any]     ,		**_a:Union[str, Any]   ):
       super().__init__(**_a   )

       self.check_model_type(
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int=None     ,		_a:str=None     ,		_a:Union[str, Any]=""     ,		**_a:Tuple   ):
       # Using "" as default argument because we're going to use `top_k=None` in user code to declare
       # "No top_k"
       snake_case__						= tokenizer_kwargs

       snake_case__						= {}
       if hasattr(self.model.config     ,		'''return_all_scores'''   ) and return_all_scores is None:
         snake_case__						= self.model.config.return_all_scores

       if isinstance(_a     ,		_a   ) or top_k is None:
         snake_case__						= top_k
         snake_case__						= False
       elif return_all_scores is not None:
         warnings.warn(
             '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
             ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''     ,		_a     ,		)
         if return_all_scores:
           snake_case__						= None
         else:
           snake_case__						= 1

       if isinstance(_a     ,		_a   ):
         snake_case__						= ClassificationFunction[function_to_apply.upper()]

       if function_to_apply is not None:
         snake_case__						= function_to_apply
       return preprocess_params, {}, postprocess_params

     def __call__(					self:List[str]     ,		*_a:str     ,		**_a:Optional[int]   ):
       snake_case__						= super().__call__(*_a     ,		**_a   )
       # TODO try and retrieve it in a nicer way from _sanitize_parameters.
       snake_case__						= '''top_k''' not in kwargs
       if isinstance(args[0]     ,		_a   ) and _legacy:
         # This pipeline is odd, and return a list when single item is run
         return [result]
       else:
         return result

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:Optional[int]     ,		**_a:str   ):
       snake_case__						= self.framework
       if isinstance(_a     ,		_a   ):
         return self.tokenizer(**_a     ,		return_tensors=_a     ,		**_a   )
       elif isinstance(_a     ,		_a   ) and len(_a   ) == 1 and isinstance(inputs[0]     ,		_a   ) and len(inputs[0]   ) == 2:
         # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
         return self.tokenizer(
             text=inputs[0][0]     ,		text_pair=inputs[0][1]     ,		return_tensors=_a     ,		**_a   )
       elif isinstance(_a     ,		_a   ):
         # This is likely an invalid usage of the pipeline attempting to pass text pairs.
         raise ValueError(
             '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
             ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''   )
       return self.tokenizer(_a     ,		return_tensors=_a     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Optional[int]   ):
       return self.model(**_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:List[Any]     ,		_a:List[Any]=None     ,		_a:Tuple=1     ,		_a:Tuple=True   ):
       # `_legacy` is used to determine if we're running the naked pipeline and in backward
       # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
       # the more natural result containing the list.
       # Default value before `set_parameters`
       if function_to_apply is None:
         if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
           snake_case__						= ClassificationFunction.SIGMOID
         elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
           snake_case__						= ClassificationFunction.SOFTMAX
         elif hasattr(self.model.config     ,		'''function_to_apply'''   ) and function_to_apply is None:
           snake_case__						= self.model.config.function_to_apply
         else:
           snake_case__						= ClassificationFunction.NONE

       snake_case__						= model_outputs['''logits'''][0]
       snake_case__						= outputs.numpy()

       if function_to_apply == ClassificationFunction.SIGMOID:
         snake_case__						= sigmoid(_a   )
       elif function_to_apply == ClassificationFunction.SOFTMAX:
         snake_case__						= softmax(_a   )
       elif function_to_apply == ClassificationFunction.NONE:
         snake_case__						= outputs
       else:
         raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}"""   )

       if top_k == 1 and _legacy:
         return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

       snake_case__						= [
           {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(_a   )
       ]
       if not _legacy:
         dict_scores.sort(key=lambda _a   : x["score"]     ,		reverse=_a   )
         if top_k is not None:
           snake_case__						= dict_scores[:top_k]
       return dict_scores





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 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							int	   =       BlenderbotConfig
     __lowercase				:							Any	   =       {}
     __lowercase				:							Optional[Any]	   =       'gelu'

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

       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= max_position_embeddings
       snake_case__						= eos_token_id
       snake_case__						= pad_token_id
       snake_case__						= bos_token_id

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size   )
       snake_case__						= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )     ,		1   )
       snake_case__						= tf.concat([input_ids, eos_tensor]     ,		axis=1   )

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

       snake_case__						= 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     ,		)
       snake_case__						= prepare_blenderbot_inputs_dict(_a     ,		_a     ,		_a   )
       return config, inputs_dict







     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Optional[Any]     ,		_a:int   ):
       snake_case__						= TFBlenderbotModel(config=_a   ).get_decoder()
       snake_case__						= inputs_dict['''input_ids''']

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

       # first forward pass
       snake_case__						= model(_a     ,		attention_mask=_a     ,		head_mask=_a     ,		use_cache=_a   )

       snake_case__				,				snake_case__						= outputs.to_tuple()

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

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

       snake_case__						= model(_a     ,		attention_mask=_a   )[0]
       snake_case__						= model(_a     ,		attention_mask=_a     ,		past_key_values=_a   )[0]

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

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

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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   __lowerCAmelCase=None							,   )    ->				Tuple:
  if attention_mask is None:
    snake_case__						= tf.cast(tf.math.not_equal(__lowerCAmelCase							,   config.pad_token_id				)							,   tf.inta				)
  if decoder_attention_mask is None:
    snake_case__						= 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:
    snake_case__						= tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    snake_case__						= tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    snake_case__						= 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 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __lowercase				:							Any	   =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __lowercase				:							Tuple	   =       (
         {
             'conversational': TFBlenderbotForConditionalGeneration,
             'feature-extraction': TFBlenderbotModel,
             'summarization': TFBlenderbotForConditionalGeneration,
             'text2text-generation': TFBlenderbotForConditionalGeneration,
             'translation': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __lowercase				:							Any	   =       True
     __lowercase				:							int	   =       False
     __lowercase				:							int	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= TFBlenderbotModelTester(self   )
       snake_case__						= ConfigTester(self     ,		config_class=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       self.config_tester.run_common_tests()







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_a   )




@require_tokenizers
@require_tf
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       ['My friends are cool but they eat too many carbs.']
     __lowercase				:							Optional[int]	   =       'facebook/blenderbot-400M-distill'

     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return BlenderbotTokenizer.from_pretrained(self.model_name   )

     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
       return model







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.tokenizer(self.src_text     ,		return_tensors='''tf'''   )
       snake_case__						= self.model.generate(
           model_inputs.input_ids     ,		)
       snake_case__						= self.tokenizer.batch_decode(generated_ids.numpy()     ,		skip_special_tokens=_a   )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )

