





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			:
			_UpperCamelCase :						Tuple     		=				BlenderbotConfig
			_UpperCamelCase :						Optional[Any]     		=				{}
			_UpperCamelCase :						List[Any]     		=				"""gelu"""





			def __init__( self					,			snake_case					,			snake_case=13					,			snake_case=7					,			snake_case=True					,			snake_case=False					,			snake_case=99					,			snake_case=32					,			snake_case=2					,			snake_case=4					,			snake_case=37					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=20					,			snake_case=2					,			snake_case=1					,			snake_case=0					,			)				->  Optional[int]:
						"""simple docstring"""





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

						a__	:						Optional[int]      		=  hidden_dropout_prob
						a__	:						List[Any]      		=  attention_probs_dropout_prob
						a__	:						Tuple      		=  max_position_embeddings
						a__	:						List[Any]      		=  eos_token_id
						a__	:						List[str]      		=  pad_token_id
						a__	:						List[Any]      		=  bos_token_id





			def 	_snake_case ( self      )				->  str:
						"""simple docstring"""





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

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

						a__	:						Dict      		=  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					,			)
						a__	:						Union[str, Any]      		=  prepare_blenderbot_inputs_dict(snake_case					,			snake_case					,			snake_case      )
						return config, inputs_dict





			def 	_snake_case ( self					,			snake_case					,			snake_case      )				->  Dict:
						"""simple docstring"""





						a__	:						Optional[int]      		=  TFBlenderbotModel(config=snake_case      ).get_decoder()
						a__	:						Optional[int]      		=  inputs_dict["input_ids"]

						a__	:						Tuple      		=  input_ids[:1, :]
						a__	:						str      		=  inputs_dict["attention_mask"][:1, :]
						a__	:						Optional[int]      		=  inputs_dict["head_mask"]
						a__	:						Any      		=  1

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

						a__	:						Any      		=  outputs.to_tuple()

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

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

						a__	:						int      		=  model(snake_case					,			attention_mask=snake_case      )[0]
						a__	:						Tuple      		=  model(snake_case					,			attention_mask=snake_case					,			past_key_values=snake_case      )[0]

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

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

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







def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None							, lowerCamelCase=None							, lowerCamelCase=None							, lowerCamelCase=None							, ):
			if attention_mask is None:
						a__	:						List[Any]      		=  tf.cast(tf.math.not_equal(lowerCamelCase							, config.pad_token_id   )							, tf.inta   )
			if decoder_attention_mask is None:
						a__	:						Union[str, Any]      		=  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:
						a__	:						Union[str, Any]      		=  tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
			if decoder_head_mask is None:
						a__	:						Dict      		=  tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
			if cross_attn_head_mask is None:
						a__	:						List[Any]      		=  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			(					_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
			_UpperCamelCase :						str     		=				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
			_UpperCamelCase :						str     		=				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
			_UpperCamelCase :						Dict     		=				(
			    {
			        """conversational""": TFBlenderbotForConditionalGeneration,
			        """feature-extraction""": TFBlenderbotModel,
			        """summarization""": TFBlenderbotForConditionalGeneration,
			        """text2text-generation""": TFBlenderbotForConditionalGeneration,
			        """translation""": TFBlenderbotForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			_UpperCamelCase :						Any     		=				True
			_UpperCamelCase :						Union[str, Any]     		=				False
			_UpperCamelCase :						Dict     		=				False





			def 	_snake_case ( self      )				->  Dict:
						"""simple docstring"""





						a__	:						str      		=  TFBlenderbotModelTester(self      )
						a__	:						Optional[int]      		=  ConfigTester(self					,			config_class=snake_case      )





			def 	_snake_case ( self      )				->  Tuple:
						"""simple docstring"""





						self.config_tester.run_common_tests()





			def 	_snake_case ( self      )				->  int:
						"""simple docstring"""





						a__	:						Any      		=  self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*snake_case      )







@require_tokenizers
@require_tf
class   __lowerCAmelCase			(					unittest.TestCase  ):
			_UpperCamelCase :						Dict     		=				["""My friends are cool but they eat too many carbs."""]
			_UpperCamelCase :						List[Any]     		=				"""facebook/blenderbot-400M-distill"""





			@cached_property
			def 	_snake_case ( self      )				->  List[Any]:
						"""simple docstring"""





						return BlenderbotTokenizer.from_pretrained(self.model_name      )





			@cached_property
			def 	_snake_case ( self      )				->  Tuple:
						"""simple docstring"""





						a__	:						int      		=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
						return model





			@slow
			def 	_snake_case ( self      )				->  Optional[Any]:
						"""simple docstring"""





						a__	:						Optional[Any]      		=  self.tokenizer(self.src_text					,			return_tensors="tf"      )
						a__	:						Optional[Any]      		=  self.model.generate(
						    model_inputs.input_ids					,			)
						a__	:						Union[str, Any]      		=  self.tokenizer.batch_decode(generated_ids.numpy()					,			skip_special_tokens=snake_case      )[0]
						assert (
						    generated_words
						    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
						)








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

