





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   snake_case_							:
  A_     				=       BlenderbotSmallConfig
  A_     				=       {}
  A_     				=       'gelu'






  def __init__(				self					:	Union[str, Any]  ,    _snake_case					:	Tuple  ,    _snake_case					:	List[Any]=13  ,    _snake_case					:	Union[str, Any]=7  ,    _snake_case					:	Optional[int]=True  ,    _snake_case					:	int=False  ,    _snake_case					:	int=99  ,    _snake_case					:	Optional[Any]=32  ,    _snake_case					:	Tuple=2  ,    _snake_case					:	List[Any]=4  ,    _snake_case					:	str=37  ,    _snake_case					:	Tuple=0.1  ,    _snake_case					:	Union[str, Any]=0.1  ,    _snake_case					:	str=20  ,    _snake_case					:	str=2  ,    _snake_case					:	Any=1  ,    _snake_case					:	Union[str, Any]=0  ,    )->int:






   '''simple docstring'''

   __lowerCAmelCase	:			Tuple         =  parent
   __lowerCAmelCase	:			str         =  batch_size
   __lowerCAmelCase	:			Dict         =  seq_length
   __lowerCAmelCase	:			Any         =  is_training
   __lowerCAmelCase	:			int         =  use_labels
   __lowerCAmelCase	:			List[str]         =  vocab_size
   __lowerCAmelCase	:			Dict         =  hidden_size
   __lowerCAmelCase	:			List[Any]         =  num_hidden_layers
   __lowerCAmelCase	:			Union[str, Any]         =  num_attention_heads
   __lowerCAmelCase	:			int         =  intermediate_size

   __lowerCAmelCase	:			Tuple         =  hidden_dropout_prob
   __lowerCAmelCase	:			Optional[int]         =  attention_probs_dropout_prob
   __lowerCAmelCase	:			Tuple         =  max_position_embeddings
   __lowerCAmelCase	:			List[str]         =  eos_token_id
   __lowerCAmelCase	:			Dict         =  pad_token_id
   __lowerCAmelCase	:			Optional[int]         =  bos_token_id






  def  UpperCAmelCase__		(				self					:	Union[str, Any]     )->Optional[Any]:






   '''simple docstring'''

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

   __lowerCAmelCase	:			Union[str, Any]         =  ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size     )

   __lowerCAmelCase	:			int         =  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	:			Optional[int]         =  prepare_blenderbot_small_inputs_dict(__UpperCAmelCase  ,    __UpperCAmelCase  ,    __UpperCAmelCase     )
   return config, inputs_dict






  def  UpperCAmelCase__		(				self					:	List[str]  ,    _snake_case					:	Tuple  ,    _snake_case					:	List[Any]     )->int:






   '''simple docstring'''

   __lowerCAmelCase	:			List[str]         =  TFBlenderbotSmallModel(config=__UpperCAmelCase     ).get_decoder()
   __lowerCAmelCase	:			Optional[int]         =  inputs_dict["""input_ids"""]

   __lowerCAmelCase	:			Any         =  input_ids[:1, :]
   __lowerCAmelCase	:			List[Any]         =  inputs_dict["""attention_mask"""][:1, :]
   __lowerCAmelCase	:			List[str]         =  inputs_dict["""head_mask"""]
   __lowerCAmelCase	:			List[Any]         =  1

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

   __lowerCAmelCase   ,	__lowerCAmelCase	:			Optional[int]         =  outputs.to_tuple()

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

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

   __lowerCAmelCase	:			int         =  model(__UpperCAmelCase  ,    attention_mask=__UpperCAmelCase     )[0]
   __lowerCAmelCase	:			List[Any]         =  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
   __lowerCAmelCase	:			Optional[int]         =  int(ids_tensor((1,)  ,    output_from_past.shape[-1]     )     )
   __lowerCAmelCase	:			Optional[int]         =  output_from_no_past[:, -3:, random_slice_idx]
   __lowerCAmelCase	:			Dict         =  output_from_past[:, :, random_slice_idx]

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






def 			_SCREAMING_SNAKE_CASE						(     SCREAMING_SNAKE_CASE						:Dict       ,       SCREAMING_SNAKE_CASE						:str       ,       SCREAMING_SNAKE_CASE						:Any       ,       SCREAMING_SNAKE_CASE						:Dict=None       ,       SCREAMING_SNAKE_CASE						:List[str]=None       ,       SCREAMING_SNAKE_CASE						:int=None       ,       SCREAMING_SNAKE_CASE						:str=None       ,       SCREAMING_SNAKE_CASE						:Union[str, Any]=None       ,       )     ->       Tuple:


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






  def  UpperCAmelCase__		(				self					:	Dict     )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase	:			List[str]         =  TFBlenderbotSmallModelTester(self     )
   __lowerCAmelCase	:			Union[str, Any]         =  ConfigTester(self  ,    config_class=__UpperCAmelCase     )






  def  UpperCAmelCase__		(				self					:	Tuple     )->Optional[int]:






   '''simple docstring'''

   self.config_tester.run_common_tests()






  def  UpperCAmelCase__		(				self					:	List[Any]     )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase	:			Any         =  self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase     )






@require_tokenizers
@require_tf
class   snake_case_							(						unittest.TestCase		):
  A_     				=       [
      '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?'
  ]
  A_     				=       'facebook/blenderbot_small-90M'






  @cached_property
  def  UpperCAmelCase__		(				self					:	Dict     )->List[str]:






   '''simple docstring'''

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






  @cached_property
  def  UpperCAmelCase__		(				self					:	Optional[int]     )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase	:			List[Any]         =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
   return model






  @slow
  def  UpperCAmelCase__		(				self					:	str     )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase	:			Optional[Any]         =  self.tokenizer(self.src_text  ,    return_tensors="""tf"""     )
   __lowerCAmelCase	:			Optional[Any]         =  self.model.generate(
       model_inputs.input_ids  ,    attention_mask=model_inputs.attention_mask  ,    num_beams=2  ,    use_cache=__UpperCAmelCase  ,    )
   __lowerCAmelCase	:			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 inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    List[Any]     ,					__UpperCAmelCase					:    Dict     ,					__UpperCAmelCase					:    Optional[Any]=1_3     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    str=2     ,					__UpperCAmelCase					:    List[Any]=3     ,					__UpperCAmelCase					:    Any=True     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    Optional[Any]=3_2     ,					__UpperCAmelCase					:    Any=5     ,					__UpperCAmelCase					:    Optional[Any]=4     ,					__UpperCAmelCase					:    Tuple=3_7     ,					__UpperCAmelCase					:    List[str]="gelu"     ,					__UpperCAmelCase					:    Union[str, Any]=0.1     ,					__UpperCAmelCase					:    List[str]=0.1     ,					__UpperCAmelCase					:    int=1_0     ,					__UpperCAmelCase					:    Any=0.02     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   patch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   is_training
						SCREAMING_SNAKE_CASE__										=   use_labels
						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_act
						SCREAMING_SNAKE_CASE__										=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE__										=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__										=   type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   initializer_range

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__										=   (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__										=   num_patches + 1

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						SCREAMING_SNAKE_CASE__										=   ViTConfig(
						    image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					hidden_size=self.hidden_size     ,					num_hidden_layers=self.num_hidden_layers     ,					num_attention_heads=self.num_attention_heads     ,					intermediate_size=self.intermediate_size     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					is_decoder=__UpperCAmelCase     ,					initializer_range=self.initializer_range     ,					)

						return config, pixel_values

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   FlaxViTModel(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE__										=   (self.image_size, self.image_size)
						SCREAMING_SNAKE_CASE__										=   (self.patch_size, self.patch_size)
						SCREAMING_SNAKE_CASE__										=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, num_patches + 1, self.hidden_size)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[Any]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__										=   FlaxViTForImageClassification(config=__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)
						self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)							)

						# test greyscale images
						SCREAMING_SNAKE_CASE__										=   1
						SCREAMING_SNAKE_CASE__										=   FlaxViTForImageClassification(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
						SCREAMING_SNAKE_CASE__										=   model(__UpperCAmelCase							)





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)				,		(
						    SCREAMING_SNAKE_CASE__
						)				,		
						)										=   config_and_inputs
						SCREAMING_SNAKE_CASE__										=   {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_flax
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     None:
						SCREAMING_SNAKE_CASE__										=   FlaxViTModelTester(self							)
						SCREAMING_SNAKE_CASE__										=   ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=3_7							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Dict:
						self.config_tester.run_common_tests()

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     str:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   inspect.signature(model.__call__							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE__										=   [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE__										=   ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__							):
												SCREAMING_SNAKE_CASE__										=   self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   model_class(__UpperCAmelCase							)

												@jax.jit
												def model_jitted(__UpperCAmelCase					:    int     ,					**__UpperCAmelCase					:    Tuple							):
															return model(pixel_values=__UpperCAmelCase     ,					**__UpperCAmelCase							)

												with self.subTest("""JIT Enabled"""							):
															SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												with self.subTest("""JIT Disabled"""							):
															with jax.disable_jit():
																		SCREAMING_SNAKE_CASE__										=   model_jitted(**__UpperCAmelCase							).to_tuple()

												self.assertEqual(len(__UpperCAmelCase							)     ,					len(__UpperCAmelCase							)							)
												for jitted_output, output in zip(__UpperCAmelCase     ,					__UpperCAmelCase							):
															self.assertEqual(jitted_output.shape     ,					output.shape							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Dict:
						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__										=   model_class_name.from_pretrained("""google/vit-base-patch16-224"""							)
									SCREAMING_SNAKE_CASE__										=   model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
									self.assertIsNotNone(__UpperCAmelCase							)


