


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      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			int							       =       BlenderbotSmallConfig
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       {}
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       "gelu"

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

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

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

          __UpperCAmelCase     :			Dict  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          __UpperCAmelCase     :			Tuple  							=      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     :			Optional[Any]  							=      prepare_blenderbot_small_inputs_dict(_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE      )
          return config, inputs_dict






     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Optional[Any]							,			snake_case					:		Any      )						->							Optional[Any]:
          __UpperCAmelCase     :			str  							=      TFBlenderbotSmallModel(config=_SCREAMING_SNAKE_CASE      ).get_decoder()
          __UpperCAmelCase     :			Optional[int]  							=      inputs_dict['''input_ids''']

          __UpperCAmelCase     :			Optional[int]  							=      input_ids[:1, :]
          __UpperCAmelCase     :			Optional[Any]  							=      inputs_dict['''attention_mask'''][:1, :]
          __UpperCAmelCase     :			Tuple  							=      inputs_dict['''head_mask''']
          __UpperCAmelCase     :			Any  							=      1

          # first forward pass
          __UpperCAmelCase     :			List[Any]  							=      model(_SCREAMING_SNAKE_CASE							,			attention_mask=_SCREAMING_SNAKE_CASE							,			head_mask=_SCREAMING_SNAKE_CASE							,			use_cache=_SCREAMING_SNAKE_CASE      )

          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      outputs.to_tuple()

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

          # append to next input_ids and
          __UpperCAmelCase     :			Tuple  							=      tf.concat([input_ids, next_tokens]							,			axis=-1      )
          __UpperCAmelCase     :			List[str]  							=      tf.concat([attention_mask, next_attn_mask]							,			axis=-1      )

          __UpperCAmelCase     :			str  							=      model(_SCREAMING_SNAKE_CASE							,			attention_mask=_SCREAMING_SNAKE_CASE      )[0]
          __UpperCAmelCase     :			int  							=      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  							=      int(ids_tensor((1,)							,			output_from_past.shape[-1]      )      )
          __UpperCAmelCase     :			int  							=      output_from_no_past[:, -3:, random_slice_idx]
          __UpperCAmelCase     :			Optional[Any]  							=      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     (						_lowercase :     List[str]      , _lowercase :     Tuple      , _lowercase :     str      , _lowercase :     Dict=None      , _lowercase :     List[Any]=None      , _lowercase :     List[str]=None      , _lowercase :     List[str]=None      , _lowercase :     Optional[Any]=None      , ):





     '''simple docstring'''






     if attention_mask is None:
          __UpperCAmelCase     :			str  							=      tf.cast(tf.math.not_equal(_A      , config.pad_token_id  )      , tf.inta  )
     if decoder_attention_mask is None:
          __UpperCAmelCase     :			Tuple  							=      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     :			Optional[Any]  							=      tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
     if decoder_head_mask is None:
          __UpperCAmelCase     :			Dict  							=      tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
     if cross_attn_head_mask is None:
          __UpperCAmelCase     :			Optional[int]  							=      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    (		lowerCAmelCase__     ,  lowerCAmelCase__     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Any							       =       (
         (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
     )
     SCREAMING_SNAKE_CASE	:			Tuple							       =       (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         {
             "conversational": TFBlenderbotSmallForConditionalGeneration,
             "feature-extraction": TFBlenderbotSmallModel,
             "summarization": TFBlenderbotSmallForConditionalGeneration,
             "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
             "translation": TFBlenderbotSmallForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE	:			int							       =       True
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       False
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       False

     def 			lowerCamelCase__			(    self					:		Dict      )						->							int:
          __UpperCAmelCase     :			str  							=      TFBlenderbotSmallModelTester(self      )
          __UpperCAmelCase     :			str  							=      ConfigTester(self							,			config_class=_SCREAMING_SNAKE_CASE      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Union[str, Any]:
          self.config_tester.run_common_tests()






     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      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	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			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?"
     ]
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       "facebook/blenderbot_small-90M"

     @cached_property
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          # use "old" tokenizer here because of bug when downloading new tokenizer
          return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )

     @cached_property
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[int]:
          __UpperCAmelCase     :			Dict  							=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
          return model






     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Tuple:
          __UpperCAmelCase     :			List[str]  							=      self.tokenizer(self.src_text							,			return_tensors='''tf'''      )
          __UpperCAmelCase     :			Any  							=      self.model.generate(
              model_inputs.input_ids							,			attention_mask=model_inputs.attention_mask							,			num_beams=2							,			use_cache=_SCREAMING_SNAKE_CASE							,			)
          __UpperCAmelCase     :			Any  							=      self.tokenizer.batch_decode(generated_ids.numpy()							,			skip_special_tokens=_SCREAMING_SNAKE_CASE      )[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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCAmelCase      :Union[str, Any]			    =       {
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "RagModel",
          "RagPreTrainedModel",
          "RagSequenceForGeneration",
          "RagTokenForGeneration",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "TFRagModel",
          "TFRagPreTrainedModel",
          "TFRagSequenceForGeneration",
          "TFRagTokenForGeneration",
      ]


if TYPE_CHECKING:
      from .configuration_rag import RagConfig
      from .retrieval_rag import RagRetriever
      from .tokenization_rag import RagTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rag import (
                TFRagModel,
                TFRagPreTrainedModel,
                TFRagSequenceForGeneration,
                TFRagTokenForGeneration,
            )

else:
      import sys

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)