




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'''







 lowercase_							       =       BlenderbotConfig
 lowercase_							       =       {}
 lowercase_							       =       "gelu"



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

       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     eos_token_id
       SCREAMING_SNAKE_CASE_      		=     pad_token_id
       SCREAMING_SNAKE_CASE_      		=     bos_token_id



 def  lowerCAmelCase_    (				self    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.seq_length - 1]							,							self.vocab_size		)
       SCREAMING_SNAKE_CASE_      		=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)							,							1		)
       SCREAMING_SNAKE_CASE_      		=     tf.concat([input_ids, eos_tensor]							,							axis=1		)

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

       SCREAMING_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							,							)
       SCREAMING_SNAKE_CASE_      		=     prepare_blenderbot_inputs_dict(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       return config, inputs_dict




 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     TFBlenderbotModel(config=_lowerCAmelCase		).get_decoder()
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['input_ids']

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

       # first forward pass
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							use_cache=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     outputs.to_tuple()

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

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

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							past_key_values=_lowerCAmelCase		)[0]

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

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

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


def    UpperCAmelCase_				( __UpperCAmelCase		:      int     ,     __UpperCAmelCase		:      List[str]     ,     __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     __UpperCAmelCase		:      List[Any]=None     ,     __UpperCAmelCase		:      str=None     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     __UpperCAmelCase		:      Dict=None     ,     )   ->					str:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.cast(tf.math.not_equal(__UpperCAmelCase     ,     config.pad_token_id				)     ,     tf.inta				)
      if decoder_attention_mask is None:
            SCREAMING_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:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            SCREAMING_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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     TFBlenderbotModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str		):
       self.config_tester.run_common_tests()




 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)





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


 '''simple docstring'''







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



 @cached_property
 def  lowerCAmelCase_    (				self    :						List[str]		):
       return BlenderbotTokenizer.from_pretrained(self.model_name		)



 @cached_property
 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
       return model




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



'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase 							=  logging.get_logger(__name__)

_UpperCamelCase 							=  {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				'beit'


     def __init__(   self			,     lowerCamelCase=8192			,     lowerCamelCase=768			,     lowerCamelCase=12			,     lowerCamelCase=12			,     lowerCamelCase=3072			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.0			,     lowerCamelCase=0.0			,     lowerCamelCase=0.02			,     lowerCamelCase=1E-12			,     lowerCamelCase=224			,     lowerCamelCase=16			,     lowerCamelCase=3			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=False			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=True			,     lowerCamelCase=[3, 5, 7, 11]			,     lowerCamelCase=[1, 2, 3, 6]			,     lowerCamelCase=True			,     lowerCamelCase=0.4			,     lowerCamelCase=256			,     lowerCamelCase=1			,     lowerCamelCase=False			,     lowerCamelCase=255			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A	:       int      =							vocab_size
          __A	:       Tuple      =							hidden_size
          __A	:       List[str]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       Optional[int]      =							hidden_act
          __A	:       str      =							hidden_dropout_prob
          __A	:       Dict      =							attention_probs_dropout_prob
          __A	:       int      =							initializer_range
          __A	:       List[str]      =							layer_norm_eps

          __A	:       str      =							image_size
          __A	:       Optional[int]      =							patch_size
          __A	:       List[Any]      =							num_channels
          __A	:       List[str]      =							use_mask_token
          __A	:       Union[str, Any]      =							use_absolute_position_embeddings
          __A	:       Optional[int]      =							use_relative_position_bias
          __A	:       int      =							use_shared_relative_position_bias
          __A	:       int      =							layer_scale_init_value
          __A	:       Union[str, Any]      =							drop_path_rate
          __A	:       Dict      =							use_mean_pooling
          # decode head attributes (semantic segmentation)
          __A	:       List[str]      =							out_indices
          __A	:       str      =							pool_scales
          # auxiliary head attributes (semantic segmentation)
          __A	:       List[str]      =							use_auxiliary_head
          __A	:       Tuple      =							auxiliary_loss_weight
          __A	:       Dict      =							auxiliary_channels
          __A	:       Tuple      =							auxiliary_num_convs
          __A	:       List[str]      =							auxiliary_concat_input
          __A	:       int      =							semantic_loss_ignore_index







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				version.parse('1.11' )


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 1E-4

