


"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class _lowerCAmelCase    :
   __UpperCAmelCase			:	Any							  =  BlenderbotSmallConfig
   __UpperCAmelCase			:	List[str]							  =  {}
   __UpperCAmelCase			:	str							  =  '''gelu'''







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=True						,     UpperCamelCase__=False						,     UpperCamelCase__=99						,     UpperCamelCase__=32						,     UpperCamelCase__=2						,     UpperCamelCase__=4						,     UpperCamelCase__=37						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=20						,     UpperCamelCase__=2						,     UpperCamelCase__=1						,     UpperCamelCase__=0						,     )     ->		Dict:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   parent
       snake_case   :				List[Any]				 =   batch_size
       snake_case   :				Tuple				 =   seq_length
       snake_case   :				str				 =   is_training
       snake_case   :				int				 =   use_labels
       snake_case   :				Tuple				 =   vocab_size
       snake_case   :				Any				 =   hidden_size
       snake_case   :				List[Any]				 =   num_hidden_layers
       snake_case   :				str				 =   num_attention_heads
       snake_case   :				int				 =   intermediate_size

       snake_case   :				str				 =   hidden_dropout_prob
       snake_case   :				Union[str, Any]				 =   attention_probs_dropout_prob
       snake_case   :				int				 =   max_position_embeddings
       snake_case   :				str				 =   eos_token_id
       snake_case   :				Dict				 =   pad_token_id
       snake_case   :				Any				 =   bos_token_id







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   ids_tensor([self.batch_size, self.seq_length - 1]						,     self.vocab_size						)
       snake_case   :				str				 =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)						,     1						)
       snake_case   :				Any				 =   tf.concat([input_ids, eos_tensor]						,     axis=1						)

       snake_case   :				int				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

       snake_case   :				str				 =   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   :				str				 =   prepare_blenderbot_small_inputs_dict(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)
       return config, inputs_dict







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Any				 =   TFBlenderbotSmallModel(config=UpperCamelCase__						).get_decoder()
       snake_case   :				Tuple				 =   inputs_dict["input_ids"]

       snake_case   :				Optional[Any]				 =   input_ids[:1, :]
       snake_case   :				Tuple				 =   inputs_dict["attention_mask"][:1, :]
       snake_case   :				List[str]				 =   inputs_dict["head_mask"]
       snake_case   :				Tuple				 =   1

       # first forward pass
       snake_case   :				str				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     head_mask=UpperCamelCase__						,     use_cache=UpperCamelCase__						)

       snake_case ,snake_case   :				Dict				 =   outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       snake_case   :				Dict				 =   ids_tensor((self.batch_size, 3)						,     config.vocab_size						)
       snake_case   :				List[Any]				 =   tf.cast(ids_tensor((self.batch_size, 3)						,     2						)						,     tf.inta						)

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

       snake_case   :				List[str]				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						)[0]
       snake_case   :				Optional[int]				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     past_key_values=UpperCamelCase__						)[0]

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

       # select random slice
       snake_case   :				Dict				 =   int(ids_tensor((1,)						,     output_from_past.shape[-1]						)						)
       snake_case   :				Union[str, Any]				 =   output_from_no_past[:, -3:, random_slice_idx]
       snake_case   :				Dict				 =   output_from_past[:, :, random_slice_idx]

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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Union[str, Any]						,							lowercase  :  int						,							lowercase  :  int=None						,							lowercase  :  Any=None						,							lowercase  :  Any=None						,							lowercase  :  Optional[Any]=None						,							lowercase  :  Optional[Any]=None						,							)     ->						str:


    """simple docstring"""





    if attention_mask is None:
        snake_case   :				Optional[Any]				 =   tf.cast(tf.math.not_equal(lowercase						,							config.pad_token_id			)						,							tf.inta			)
    if decoder_attention_mask is None:
        snake_case   :				Dict				 =   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   :				Optional[Any]				 =   tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
    if decoder_head_mask is None:
        snake_case   :				int				 =   tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
    if cross_attn_head_mask is None:
        snake_case   :				List[str]				 =   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    (				snake_case_   ,							snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Optional[Any]							  =  (
       (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
   )
   __UpperCAmelCase			:	Optional[Any]							  =  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
   __UpperCAmelCase			:	Optional[int]							  =  (
       {
           '''conversational''': TFBlenderbotSmallForConditionalGeneration,
           '''feature-extraction''': TFBlenderbotSmallModel,
           '''summarization''': TFBlenderbotSmallForConditionalGeneration,
           '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
           '''translation''': TFBlenderbotSmallForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase			:	Optional[int]							  =  True
   __UpperCAmelCase			:	Any							  =  False
   __UpperCAmelCase			:	Tuple							  =  False







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				str				 =   TFBlenderbotSmallModelTester(self						)
       snake_case   :				Optional[int]				 =   ConfigTester(self						,     config_class=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__						)







@require_tokenizers
@require_tf
class _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[str]							  =  [
       '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
       '''  i\'m going to throw up.\nand why is that?'''
   ]
   __UpperCAmelCase			:	Tuple							  =  '''facebook/blenderbot_small-90M'''







   @cached_property
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"						)







   @cached_property
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				str				 =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
       return model







   @slow
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self.tokenizer(self.src_text						,     return_tensors="tf"						)
       snake_case   :				List[Any]				 =   self.model.generate(
           model_inputs.input_ids						,     attention_mask=model_inputs.attention_mask						,     num_beams=2						,     use_cache=UpperCamelCase__						,     )
       snake_case   :				List[Any]				 =   self.tokenizer.batch_decode(generated_ids.numpy()						,     skip_special_tokens=UpperCamelCase__						)[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )









"""simple docstring"""



import copy
import os
from typing import Union

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


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {
    """BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Tuple							  =  '''altclip_text_model'''







   def __init__(     self						,     UpperCamelCase__=25_0002						,     UpperCamelCase__=1024						,     UpperCamelCase__=24						,     UpperCamelCase__=16						,     UpperCamelCase__=4096						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=514						,     UpperCamelCase__=1						,     UpperCamelCase__=0.02						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1e-05						,     UpperCamelCase__=1						,     UpperCamelCase__=0						,     UpperCamelCase__=2						,     UpperCamelCase__="absolute"						,     UpperCamelCase__=True						,     UpperCamelCase__=768						,     **UpperCamelCase__						,     )     ->		Optional[int]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,     bos_token_id=UpperCamelCase__						,     eos_token_id=UpperCamelCase__						,     **UpperCamelCase__						)

       snake_case   :				Any				 =   vocab_size
       snake_case   :				List[Any]				 =   hidden_size
       snake_case   :				Optional[int]				 =   num_hidden_layers
       snake_case   :				Optional[Any]				 =   num_attention_heads
       snake_case   :				Dict				 =   hidden_act
       snake_case   :				Dict				 =   intermediate_size
       snake_case   :				int				 =   hidden_dropout_prob
       snake_case   :				Optional[int]				 =   attention_probs_dropout_prob
       snake_case   :				Union[str, Any]				 =   max_position_embeddings
       snake_case   :				Optional[int]				 =   type_vocab_size
       snake_case   :				Dict				 =   initializer_range
       snake_case   :				int				 =   initializer_factor
       snake_case   :				Union[str, Any]				 =   layer_norm_eps
       snake_case   :				List[Any]				 =   position_embedding_type
       snake_case   :				Any				 =   use_cache
       snake_case   :				str				 =   project_dim







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Tuple							  =  '''altclip_vision_model'''







   def __init__(     self						,     UpperCamelCase__=768						,     UpperCamelCase__=3072						,     UpperCamelCase__=512						,     UpperCamelCase__=12						,     UpperCamelCase__=12						,     UpperCamelCase__=3						,     UpperCamelCase__=224						,     UpperCamelCase__=32						,     UpperCamelCase__="quick_gelu"						,     UpperCamelCase__=1e-5						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1.0						,     **UpperCamelCase__						,     )     ->		str:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)

       snake_case   :				Optional[int]				 =   hidden_size
       snake_case   :				str				 =   intermediate_size
       snake_case   :				List[str]				 =   projection_dim
       snake_case   :				Optional[Any]				 =   num_hidden_layers
       snake_case   :				Optional[int]				 =   num_attention_heads
       snake_case   :				str				 =   num_channels
       snake_case   :				List[str]				 =   patch_size
       snake_case   :				List[Any]				 =   image_size
       snake_case   :				Union[str, Any]				 =   initializer_range
       snake_case   :				Optional[Any]				 =   initializer_factor
       snake_case   :				Any				 =   attention_dropout
       snake_case   :				Dict				 =   layer_norm_eps
       snake_case   :				List[str]				 =   hidden_act







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		"PretrainedConfig":

       '''simple docstring'''




       cls._set_token_in_kwargs(UpperCamelCase__						)

       snake_case ,snake_case   :				str				 =   cls.get_config_dict(UpperCamelCase__						,     **UpperCamelCase__						)

       # get the vision config dict if we are loading from AltCLIPConfig
       if config_dict.get("model_type"						) == "altclip":
           snake_case   :				Optional[Any]				 =   config_dict["vision_config"]

       if "model_type" in config_dict and hasattr(cls						,     "model_type"						) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

       return cls.from_dict(UpperCamelCase__						,     **UpperCamelCase__						)







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	str							  =  '''altclip'''
   __UpperCAmelCase			:	Optional[Any]							  =  True







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=768						,     UpperCamelCase__=2.6592						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[str]				 =   kwargs.pop("text_config_dict"						,     UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   kwargs.pop("vision_config_dict"						,     UpperCamelCase__						)

       super().__init__(**UpperCamelCase__						)

       # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
       # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
       # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
       if text_config_dict is not None:
           if text_config is None:
               snake_case   :				List[str]				 =   {}

           # This is the complete result when using `text_config_dict`.
           snake_case   :				Dict				 =   AltCLIPTextConfig(**UpperCamelCase__						).to_dict()

           # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
           for key, value in _text_config_dict.items():
               if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                   # If specified in `text_config_dict`
                   if key in text_config_dict:
                       snake_case   :				Optional[Any]				 =   (
                           F'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
                           F'The value `text_config_dict["{key}"]` will be used instead.'
                       )
                   # If inferred from default argument values (just to be super careful)
                   else:
                       snake_case   :				Any				 =   (
                           F'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
                           F'value `text_config["{key}"]` will be overriden.'
                       )
                   logger.warning(UpperCamelCase__						)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
           text_config.update(_text_config_dict						)

       if vision_config_dict is not None:
           if vision_config is None:
               snake_case   :				Union[str, Any]				 =   {}

           # This is the complete result when using `vision_config_dict`.
           snake_case   :				int				 =   AltCLIPVisionConfig(**UpperCamelCase__						).to_dict()
           # convert keys to string instead of integer
           if "id2label" in _vision_config_dict:
               snake_case   :				Optional[int]				 =   {
                   str(UpperCamelCase__						): value for key, value in _vision_config_dict["id2label"].items()
               }

           # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
           for key, value in _vision_config_dict.items():
               if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                   # If specified in `vision_config_dict`
                   if key in vision_config_dict:
                       snake_case   :				int				 =   (
                           F'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
                           F'values. The value `vision_config_dict["{key}"]` will be used instead.'
                       )
                   # If inferred from default argument values (just to be super careful)
                   else:
                       snake_case   :				Optional[Any]				 =   (
                           F'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
                           F'The value `vision_config["{key}"]` will be overriden.'
                       )
                   logger.warning(UpperCamelCase__						)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
           vision_config.update(_vision_config_dict						)

       if text_config is None:
           snake_case   :				Optional[int]				 =   {}
           logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."						)

       if vision_config is None:
           snake_case   :				Dict				 =   {}
           logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."						)

       snake_case   :				Dict				 =   AltCLIPTextConfig(**UpperCamelCase__						)
       snake_case   :				Tuple				 =   AltCLIPVisionConfig(**UpperCamelCase__						)

       snake_case   :				int				 =   projection_dim
       snake_case   :				List[str]				 =   logit_scale_init_value
       snake_case   :				int				 =   1.0







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       return cls(text_config=text_config.to_dict()						,     vision_config=vision_config.to_dict()						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   copy.deepcopy(self.__dict__						)
       snake_case   :				Optional[int]				 =   self.text_config.to_dict()
       snake_case   :				str				 =   self.vision_config.to_dict()
       snake_case   :				Optional[int]				 =   self.__class__.model_type
       return output





