





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=sys.maxsize						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      'bilinear'
									_UpperCAmelCase					=      max_size
									_UpperCAmelCase					=      short_edge_length






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      []
									for img in imgs:
																_UpperCAmelCase		,	_UpperCAmelCase					=      img.shape[:2]
																# later: provide list and randomly choose index for resize
																_UpperCAmelCase					=      np.random.randint(self.short_edge_length[0]      ,   self.short_edge_length[1] + 1						)
																if size == 0:
																							return img
																_UpperCAmelCase					=      size * 1.0 / min(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																if h < w:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      size, scale * w
																else:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      scale * h, size
																if max(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) > self.max_size:
																							_UpperCAmelCase					=      self.max_size * 1.0 / max(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      newh * scale
																							_UpperCAmelCase					=      neww * scale
																_UpperCAmelCase					=      int(neww + 0.5						)
																_UpperCAmelCase					=      int(newh + 0.5						)

																if img.dtype == np.uinta:
																							_UpperCAmelCase					=      Image.fromarray(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      pil_image.resize((neww, newh)      ,   PILImageResampling.BILINEAR						)
																							_UpperCAmelCase					=      np.asarray(_SCREAMING_SNAKE_CASE						)
																else:
																							_UpperCAmelCase					=      img.permute(2      ,   0      ,   1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																							_UpperCAmelCase					=      nn.functional.interpolate(
																							    _SCREAMING_SNAKE_CASE      ,   (newh, neww)      ,   mode=self.interp_method      ,   align_corners=_SCREAMING_SNAKE_CASE						).squeeze(0						)
																img_augs.append(_SCREAMING_SNAKE_CASE						)

									return img_augs



class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,   cfg.INPUT.MAX_SIZE_TEST						)
									_UpperCAmelCase					=      cfg.INPUT.FORMAT
									_UpperCAmelCase					=      cfg.SIZE_DIVISIBILITY
									_UpperCAmelCase					=      cfg.PAD_VALUE
									_UpperCAmelCase					=      cfg.INPUT.MAX_SIZE_TEST
									_UpperCAmelCase					=      cfg.MODEL.DEVICE
									_UpperCAmelCase					=      torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)      ,   1      ,   1						)
									_UpperCAmelCase					=      torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)      ,   1      ,   1						)
									_UpperCAmelCase					=      lambda _SCREAMING_SNAKE_CASE						: (x - self.pixel_mean) / self.pixel_std
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      tuple(max(_SCREAMING_SNAKE_CASE						) for s in zip(*[img.shape for img in images]						)						)
									_UpperCAmelCase					=      [im.shape[-2:] for im in images]
									_UpperCAmelCase					=      [
									    nn.functional.pad(
									        _SCREAMING_SNAKE_CASE      ,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,   value=self.pad_value      ,   )
									    for size, im in zip(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									]

									return torch.stack(_SCREAMING_SNAKE_CASE						), torch.tensor(_SCREAMING_SNAKE_CASE						)






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	List[Any]:




									"""simple docstring"""





									with torch.no_grad():
																if not isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																							_UpperCAmelCase					=      [images]
																if single_image:
																							assert len(_SCREAMING_SNAKE_CASE						) == 1
																for i in range(len(_SCREAMING_SNAKE_CASE						)						):
																							if isinstance(images[i]      ,   torch.Tensor						):
																														images.insert(_SCREAMING_SNAKE_CASE      ,   images.pop(_SCREAMING_SNAKE_CASE						).to(self.device						).float()						)
																							elif not isinstance(images[i]      ,   torch.Tensor						):
																														images.insert(
																														    _SCREAMING_SNAKE_CASE      ,   torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE						)      ,   input_format=self.input_format						)						)
																														    .to(self.device						)
																														    .float()      ,   )
            # resize smallest edge
																_UpperCAmelCase					=      torch.tensor([im.shape[:2] for im in images]						)
																_UpperCAmelCase					=      self.aug(_SCREAMING_SNAKE_CASE						)
																# transpose images and convert to torch tensors
																# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																# now normalize before pad to avoid useless arithmetic
																_UpperCAmelCase					=      [self.normalizer(_SCREAMING_SNAKE_CASE						) for x in images]
																# now pad them to do the following operations
																_UpperCAmelCase		,	_UpperCAmelCase					=      self.pad(_SCREAMING_SNAKE_CASE						)
																# Normalize

																if self.size_divisibility > 0:
																							raise NotImplementedError()
																# pad
																_UpperCAmelCase					=      torch.true_divide(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																if single_image:
																							return images[0], sizes[0], scales_yx[0]
																else:
																							return images, sizes, scales_yx



def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Union[str, Any]	)  ->		Optional[Any]:


							'''simple docstring'''





							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		Tuple[int, int]	)  ->		Any:


							'''simple docstring'''





							assert torch.isfinite(a__	).all(), "Box tensor contains infinite or NaN!"
							_UpperCAmelCase		,	_UpperCAmelCase					=      box_size
							tensor[:, 0].clamp_(min=0      ,  max=a__	)
							tensor[:, 1].clamp_(min=0      ,  max=a__	)
							tensor[:, 2].clamp_(min=0      ,  max=a__	)
							tensor[:, 3].clamp_(min=0      ,  max=a__	)




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 		__a				:
		_a     :  Dict           =     BlenderbotConfig
		_a     :  Dict           =     {}
		_a     :  Union[str, Any]           =     'gelu'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=20      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=0      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      intermediate_size

									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      eos_token_id
									_UpperCAmelCase					=      pad_token_id
									_UpperCAmelCase					=      bos_token_id
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size						)
									_UpperCAmelCase					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)      ,   1						)
									_UpperCAmelCase					=      tf.concat([input_ids, eos_tensor]      ,   axis=1						)

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

									_UpperCAmelCase					=      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					=      prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return config, inputs_dict






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFBlenderbotModel(config=_SCREAMING_SNAKE_CASE						).get_decoder()
									_UpperCAmelCase					=      inputs_dict['input_ids']

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

									# first forward pass
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   head_mask=_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase		,	_UpperCAmelCase					=      outputs.to_tuple()

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

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

									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)[0]
									_UpperCAmelCase					=      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(ids_tensor((1,)      ,   output_from_past.shape[-1]						)						)
									_UpperCAmelCase					=      output_from_no_past[:, -3:, random_slice_idx]
									_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		Dict      ,  a__:		Any      ,  a__:		Any=None      ,  a__:		List[Any]=None      ,  a__:		Union[str, Any]=None      ,  a__:		Tuple=None      ,  a__:		Union[str, Any]=None      ,  )  ->		Any:


							'''simple docstring'''





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




									"""simple docstring"""





									_UpperCAmelCase					=      TFBlenderbotModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									self.config_tester.run_common_tests()






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




									"""simple docstring"""





									_UpperCAmelCase					=      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      ):
		_a     :  int           =     ['My friends are cool but they eat too many carbs.']
		_a     :  List[Any]           =     'facebook/blenderbot-400M-distill'
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return BlenderbotTokenizer.from_pretrained(self.model_name						)
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model






		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





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