






"""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__       :






      '''simple docstring'''

      __UpperCamelCase							=						BlenderbotSmallConfig
      __UpperCamelCase							=						{}
      __UpperCamelCase							=						"gelu"
      def __init__(		self  :						Optional[Any]		,			lowercase_  :						int		,			lowercase_  :						Optional[Any]=13		,			lowercase_  :						Union[str, Any]=7		,			lowercase_  :						Any=True		,			lowercase_  :						Optional[int]=False		,			lowercase_  :						str=99		,			lowercase_  :						Optional[int]=32		,			lowercase_  :						Optional[Any]=2		,			lowercase_  :						str=4		,			lowercase_  :						Union[str, Any]=37		,			lowercase_  :						List[Any]=0.1		,			lowercase_  :						Any=0.1		,			lowercase_  :						Dict=20		,			lowercase_  :						Optional[Any]=2		,			lowercase_  :						Any=1		,			lowercase_  :						Optional[int]=0		,			):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						str							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Tuple							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						str							=	eos_token_id
          SCREAMING_SNAKE_CASE_   :						List[str]							=	pad_token_id
          SCREAMING_SNAKE_CASE_   :						str							=	bos_token_id
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	ids_tensor([self.batch_size, self.seq_length - 1]		,			self.vocab_size)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)		,			1)
          SCREAMING_SNAKE_CASE_   :						Dict							=	tf.concat([input_ids, eos_tensor]		,			axis=1)

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

          SCREAMING_SNAKE_CASE_   :						Any							=	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_   :						int							=	prepare_blenderbot_small_inputs_dict(UpperCamelCase_		,			UpperCamelCase_		,			UpperCamelCase_)
          return config, inputs_dict



      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	TFBlenderbotSmallModel(config=UpperCamelCase_).get_decoder()
          SCREAMING_SNAKE_CASE_   :						str							=	inputs_dict['input_ids']

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	input_ids[:1, :]
          SCREAMING_SNAKE_CASE_   :						List[str]							=	inputs_dict['attention_mask'][:1, :]
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	inputs_dict['head_mask']
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	1

          # first forward pass
          SCREAMING_SNAKE_CASE_   :						Any							=	model(UpperCamelCase_		,			attention_mask=UpperCamelCase_		,			head_mask=UpperCamelCase_		,			use_cache=UpperCamelCase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	outputs.to_tuple()

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

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

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	model(UpperCamelCase_		,			attention_mask=UpperCamelCase_)[0]
          SCREAMING_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
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	int(ids_tensor((1,)		,			output_from_past.shape[-1]))
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	output_from_no_past[:, -3:, random_slice_idx]
          SCREAMING_SNAKE_CASE_   :						Any							=	output_from_past[:, :, random_slice_idx]

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



def       _A    (__a					,       __a					,       __a					,       __a=None					,       __a=None					,       __a=None					,       __a=None					,       __a=None					,       )				-> Tuple:
    """simple docstring"""






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






      '''simple docstring'''

      __UpperCamelCase							=						(
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      __UpperCamelCase							=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      __UpperCamelCase							=						(
          {
              "conversational": TFBlenderbotSmallForConditionalGeneration,
              "feature-extraction": TFBlenderbotSmallModel,
              "summarization": TFBlenderbotSmallForConditionalGeneration,
              "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
              "translation": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      __UpperCamelCase							=						True
      __UpperCamelCase							=						False
      __UpperCamelCase							=						False
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	TFBlenderbotSmallModelTester(self)
          SCREAMING_SNAKE_CASE_   :						Tuple							=	ConfigTester(self		,			config_class=UpperCamelCase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          self.config_tester.run_common_tests()



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	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				):






      '''simple docstring'''

      __UpperCamelCase							=						[
          "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							=						"facebook/blenderbot_small-90M"
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
      @cached_property
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
          return model



      @slow
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.tokenizer(self.src_text		,			return_tensors='''tf''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.model.generate(
              model_inputs.input_ids		,			attention_mask=model_inputs.attention_mask		,			num_beams=2		,			use_cache=UpperCamelCase_		,			)
          SCREAMING_SNAKE_CASE_   :						Optional[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"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__				)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			*lowercase_  :						Any		,			**lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(*lowercase_		,			**lowercase_)
          requires_backends(self		,			'''vision''')
          self.check_model_type(lowercase_)
      def __call__(		self  :						int		,			lowercase_  :						Union[str, List[str], "Image.Image", List["Image.Image"]]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          return super().__call__(lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			**lowercase_  :						List[str]):




          '''simple docstring'''


          return {}, {}, {}
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	load_image(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image.size
          SCREAMING_SNAKE_CASE_   :						int							=	self.image_processor(images=lowercase_		,			return_tensors=self.framework)
          return model_inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model(**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_outputs.predicted_depth
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,			size=self.image_size[::-1]		,			mode='''bicubic'''		,			align_corners=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	prediction.squeeze().cpu().numpy()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	(output * 255 / np.max(lowercase_)).astype('''uint8''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Image.fromarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	predicted_depth
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	depth
          return output_dict


