

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_pytesseract_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor



class 					lowercase						(  unittest.TestCase ):


							def __init__(			self    ,			_snake_case    ,			_snake_case=7    ,			_snake_case=3    ,			_snake_case=18    ,			_snake_case=30    ,			_snake_case=400    ,			_snake_case=True    ,			_snake_case=None    ,			_snake_case=True    ,			)							->				Tuple:
								UpperCAmelCase_		:      Union[str, Any]       	=					size if size is not None else {'height': 18, 'width': 18}
								UpperCAmelCase_		:      int       	=					parent
								UpperCAmelCase_		:      Union[str, Any]       	=					batch_size
								UpperCAmelCase_		:      Dict       	=					num_channels
								UpperCAmelCase_		:      int       	=					image_size
								UpperCAmelCase_		:      str       	=					min_resolution
								UpperCAmelCase_		:      List[str]       	=					max_resolution
								UpperCAmelCase_		:      List[Any]       	=					do_resize
								UpperCAmelCase_		:      str       	=					size
								UpperCAmelCase_		:      str       	=					apply_ocr





							def 						_snake_case							(			self)							->				str:
								return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      Optional[int]=   LayoutLMvaImageProcessor if is_pytesseract_available() else None


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      str       	=					LayoutLMvaImageProcessingTester(self)


							@property
							def 						_snake_case							(			self)							->				Any:
								return self.image_processor_tester.prepare_image_processor_dict()


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Dict       	=					self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_snake_case    ,			'do_resize'))
								self.assertTrue(hasattr(_snake_case    ,			'size'))
								self.assertTrue(hasattr(_snake_case    ,			'apply_ocr'))


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      Any       	=					self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size    ,			{'height': 18, 'width': 18})

								UpperCAmelCase_		:      str       	=					self.image_processing_class.from_dict(self.image_processor_dict    ,			size=42)
								self.assertEqual(image_processor.size    ,			{'height': 42, 'width': 42})


							def 						_snake_case							(			self)							->				List[Any]:
								pass


							def 						_snake_case							(			self)							->				List[Any]:
								# Initialize image_processing
								UpperCAmelCase_		:      List[Any]       	=					self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase_		:      Optional[int]       	=					prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case    ,			Image.Image)

								# Test not batched input
								UpperCAmelCase_		:      Optional[Any]       	=					image_processing(image_inputs[0]    ,			return_tensors='pt')
								self.assertEqual(
								    encoding.pixel_values.shape    ,			(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,			)

								self.assertIsInstance(encoding.words    ,			_snake_case)
								self.assertIsInstance(encoding.boxes    ,			_snake_case)

								# Test batched
								UpperCAmelCase_		:      str       	=					image_processing(_snake_case    ,			return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,			(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,			)


							def 						_snake_case							(			self)							->				str:
								# Initialize image_processing
								UpperCAmelCase_		:      str       	=					self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase_		:      Any       	=					prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_snake_case    ,			numpify=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case    ,			np.ndarray)

								# Test not batched input
								UpperCAmelCase_		:      Union[str, Any]       	=					image_processing(image_inputs[0]    ,			return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,			(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,			)

								# Test batched
								UpperCAmelCase_		:      Optional[Any]       	=					image_processing(_snake_case    ,			return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,			(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,			)


							def 						_snake_case							(			self)							->				List[str]:
								# Initialize image_processing
								UpperCAmelCase_		:      Any       	=					self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase_		:      List[str]       	=					prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_snake_case    ,			torchify=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case    ,			torch.Tensor)

								# Test not batched input
								UpperCAmelCase_		:      Tuple       	=					image_processing(image_inputs[0]    ,			return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,			(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,			)

								# Test batched
								UpperCAmelCase_		:      str       	=					image_processing(_snake_case    ,			return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape    ,			(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )    ,			)





							def 						_snake_case							(			self)							->				int:
								# with apply_OCR = True
								UpperCAmelCase_		:      Union[str, Any]       	=					LayoutLMvaImageProcessor()

								from datasets import load_dataset

								UpperCAmelCase_		:      str       	=					load_dataset('hf-internal-testing/fixtures_docvqa'    ,			split='test')

								UpperCAmelCase_		:      str       	=					Image.open(ds[0]['file']).convert('RGB')

								UpperCAmelCase_		:      List[Any]       	=					image_processing(_snake_case    ,			return_tensors='pt')

								self.assertEqual(encoding.pixel_values.shape    ,			(1, 3, 224, 224))
								self.assertEqual(len(encoding.words)    ,			len(encoding.boxes))

								# fmt: off
								# the words and boxes were obtained with Tesseract 4.1.1
								UpperCAmelCase_		:      Any       	=					[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
								UpperCAmelCase_		:      Tuple       	=					[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
								# fmt: on

								self.assertListEqual(encoding.words    ,			_snake_case)
								self.assertListEqual(encoding.boxes    ,			_snake_case)

								# with apply_OCR = False
								UpperCAmelCase_		:      Tuple       	=					LayoutLMvaImageProcessor(apply_ocr=_snake_case)

								UpperCAmelCase_		:      str       	=					image_processing(_snake_case    ,			return_tensors='pt')

								self.assertEqual(encoding.pixel_values.shape    ,			(1, 3, 224, 224))









'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

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

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						lowerCAmelCase__										= "platform"

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,)    ->    List[Any]:
	if attention_mask is None:
		UpperCAmelCase_		:      Tuple       	=					np.where(input_ids != config.pad_token_id   ,1   ,0  )
	if decoder_attention_mask is None:
		UpperCAmelCase_		:      List[str]       	=					np.where(decoder_input_ids != config.pad_token_id   ,1   ,0  )
	if head_mask is None:
		UpperCAmelCase_		:      Optional[Any]       	=					np.ones((config.encoder_layers, config.encoder_attention_heads)  )
	if decoder_head_mask is None:
		UpperCAmelCase_		:      Optional[int]       	=					np.ones((config.decoder_layers, config.decoder_attention_heads)  )
	if cross_attn_head_mask is None:
		UpperCAmelCase_		:      List[str]       	=					np.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": attention_mask,
	}



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=99    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=4    ,			_snake_case=4    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=32    ,			_snake_case=2    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=0.02    ,			)							->				int:
								UpperCAmelCase_		:      List[Any]       	=					parent
								UpperCAmelCase_		:      Optional[int]       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					seq_length
								UpperCAmelCase_		:      List[Any]       	=					is_training
								UpperCAmelCase_		:      int       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      Dict       	=					hidden_size
								UpperCAmelCase_		:      int       	=					num_hidden_layers
								UpperCAmelCase_		:      int       	=					num_attention_heads
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Dict       	=					hidden_act
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Any       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      int       	=					max_position_embeddings
								UpperCAmelCase_		:      Optional[Any]       	=					eos_token_id
								UpperCAmelCase_		:      Tuple       	=					pad_token_id
								UpperCAmelCase_		:      List[Any]       	=					bos_token_id
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      int       	=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size)    ,			3    ,			self.vocab_size)
								UpperCAmelCase_		:      List[str]       	=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,			dtype=np.intaa))    ,			-1)

								UpperCAmelCase_		:      Optional[int]       	=					shift_tokens_right(_snake_case    ,			1    ,			2)

								UpperCAmelCase_		:      Dict       	=					BlenderbotSmallConfig(
								    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_id=self.eos_token_id    ,			bos_token_id=self.bos_token_id    ,			pad_token_id=self.pad_token_id    ,			initializer_range=self.initializer_range    ,			use_cache=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					prepare_blenderbot_inputs_dict(_snake_case    ,			_snake_case    ,			_snake_case)
								return config, inputs_dict


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					self.prepare_config_and_inputs()
								return config, inputs_dict


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Optional[Any]       	=					20
								UpperCAmelCase_		:      str       	=					model_class_name(_snake_case)

								UpperCAmelCase_		:      Dict       	=					model.encode(inputs_dict['input_ids'])

								UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					(
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								UpperCAmelCase_		:      Optional[int]       	=					model.init_cache(decoder_input_ids.shape[0]    ,			_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,			dtype='i4')

								UpperCAmelCase_		:      Dict       	=					jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]    ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					model.decode(
								    decoder_input_ids[:, :-1]    ,			_snake_case    ,			decoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			decoder_position_ids=_snake_case    ,			)

								UpperCAmelCase_		:      int       	=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,			dtype='i4')
								UpperCAmelCase_		:      Any       	=					model.decode(
								    decoder_input_ids[:, -1:]    ,			_snake_case    ,			decoder_attention_mask=_snake_case    ,			past_key_values=outputs_cache.past_key_values    ,			decoder_position_ids=_snake_case    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					model.decode(_snake_case    ,			_snake_case)

								UpperCAmelCase_		:      List[str]       	=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1e-3    ,			msg=F"""Max diff is {diff}""")





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      int       	=					20
								UpperCAmelCase_		:      Optional[int]       	=					model_class_name(_snake_case)

								UpperCAmelCase_		:      Optional[int]       	=					model.encode(inputs_dict['input_ids'])

								UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					(
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								UpperCAmelCase_		:      int       	=					jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
								    ]    ,			axis=-1    ,			)

								UpperCAmelCase_		:      Any       	=					model.init_cache(decoder_input_ids.shape[0]    ,			_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]    ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					model.decode(
								    decoder_input_ids[:, :-1]    ,			_snake_case    ,			decoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			decoder_position_ids=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,			dtype='i4')
								UpperCAmelCase_		:      Dict       	=					model.decode(
								    decoder_input_ids[:, -1:]    ,			_snake_case    ,			past_key_values=outputs_cache.past_key_values    ,			decoder_attention_mask=_snake_case    ,			decoder_position_ids=_snake_case    ,			)

								UpperCAmelCase_		:      Any       	=					model.decode(_snake_case    ,			_snake_case    ,			decoder_attention_mask=_snake_case)

								UpperCAmelCase_		:      Dict       	=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1e-3    ,			msg=F"""Max diff is {diff}""")



@require_flax
class 					lowercase						(  unittest.TestCase ):
							_lowerCamelCase		:      Union[str, Any]=   99


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      str       	=					np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ]    ,			dtype=np.intaa    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					input_ids.shape[0]
								UpperCAmelCase_		:      Any       	=					BlenderbotSmallConfig(
								    vocab_size=self.vocab_size    ,			d_model=24    ,			encoder_layers=2    ,			decoder_layers=2    ,			encoder_attention_heads=2    ,			decoder_attention_heads=2    ,			encoder_ffn_dim=32    ,			decoder_ffn_dim=32    ,			max_position_embeddings=48    ,			eos_token_id=2    ,			pad_token_id=1    ,			bos_token_id=0    ,			)
								return config, input_ids, batch_size


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					self._get_config_and_data()
								UpperCAmelCase_		:      str       	=					FlaxBlenderbotSmallForConditionalGeneration(_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					lm_model(input_ids=_snake_case)
								UpperCAmelCase_		:      str       	=					(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['logits'].shape    ,			_snake_case)


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Tuple       	=					BlenderbotSmallConfig(
								    vocab_size=self.vocab_size    ,			d_model=14    ,			encoder_layers=2    ,			decoder_layers=2    ,			encoder_attention_heads=2    ,			decoder_attention_heads=2    ,			encoder_ffn_dim=8    ,			decoder_ffn_dim=8    ,			max_position_embeddings=48    ,			)
								UpperCAmelCase_		:      Union[str, Any]       	=					FlaxBlenderbotSmallForConditionalGeneration(_snake_case)
								UpperCAmelCase_		:      Dict       	=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,			dtype=np.intaa)
								UpperCAmelCase_		:      Union[str, Any]       	=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,			dtype=np.intaa)
								UpperCAmelCase_		:      List[str]       	=					lm_model(input_ids=_snake_case    ,			decoder_input_ids=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['logits'].shape    ,			_snake_case)





							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      int       	=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,			dtype=np.intaa)
								UpperCAmelCase_		:      List[str]       	=					shift_tokens_right(_snake_case    ,			1    ,			2)
								UpperCAmelCase_		:      Dict       	=					np.equal(_snake_case    ,			1).astype(np.floataa).sum()
								UpperCAmelCase_		:      Union[str, Any]       	=					np.equal(_snake_case    ,			1).astype(np.floataa).sum()
								self.assertEqual(shifted.shape    ,			input_ids.shape)
								self.assertEqual(_snake_case    ,			n_pad_before - 1)
								self.assertTrue(np.equal(shifted[:, 0]    ,			2).all())



@require_flax
class 					lowercase						(  a_,					unittest.TestCase,					a_ ):
							_lowerCamelCase		:      str=   True
							_lowerCamelCase		:      Union[str, Any]=   (
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							_lowerCamelCase		:      Optional[int]=   (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      List[str]       	=					FlaxBlenderbotSmallModelTester(self)


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:      Dict       	=					self._prepare_for_class(_snake_case    ,			_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					model_class(_snake_case)

										@jax.jit
										def encode_jitted(_snake_case    ,			_snake_case=None    ,			**_snake_case):
											return model.encode(input_ids=_snake_case    ,			attention_mask=_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:      Optional[int]       	=					encode_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:      Optional[int]       	=					encode_jitted(**_snake_case).to_tuple()

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


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:      Tuple       	=					model_class(_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					model.encode(inputs_dict['input_ids']    ,			inputs_dict['attention_mask'])

										UpperCAmelCase_		:      str       	=					{
										    'decoder_input_ids': inputs_dict['decoder_input_ids'],
										    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
										    'encoder_outputs': encoder_outputs,
										}

										@jax.jit
										def decode_jitted(_snake_case    ,			_snake_case    ,			_snake_case):
											return model.decode(
											    decoder_input_ids=_snake_case    ,			decoder_attention_mask=_snake_case    ,			encoder_outputs=_snake_case    ,			)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:      Tuple       	=					decode_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:      List[Any]       	=					decode_jitted(**_snake_case).to_tuple()

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





							@slow
							def 						_snake_case							(			self)							->				Any:
								for model_class_name in self.all_model_classes:
									UpperCAmelCase_		:      Any       	=					model_class_name.from_pretrained('facebook/blenderbot_small-90M')
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									UpperCAmelCase_		:      List[Any]       	=					np.ones((1, 1)) * model.config.eos_token_id
									UpperCAmelCase_		:      Dict       	=					model(_snake_case)
									self.assertIsNotNone(_snake_case)






