
'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
		UpperCAmelCase__				:	List[Any]					=						"platform"

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right







def 					A       ( UpperCamelCase_   :       Union[str, Any]  ,		UpperCamelCase_   :       List[str]  ,		UpperCamelCase_   :       Optional[Any]=None  ,		UpperCamelCase_   :       int=None  ,		UpperCamelCase_   :       Optional[Any]=None  ,		UpperCamelCase_   :       Any=None  ,		UpperCamelCase_   :       Any=None  ,		UpperCamelCase_   :       Optional[int]=None  ,		)   ->    str:







			'''simple docstring'''



			if attention_mask is None:
						lowerCAmelCase__									=							np.where(input_ids != config.pad_token_id  ,		1  ,		0 )
			if decoder_attention_mask is None:
						lowerCAmelCase__									=							np.where(decoder_input_ids != config.pad_token_id  ,		1  ,		0 )
			if head_mask is None:
						lowerCAmelCase__									=							np.ones((config.encoder_layers, config.encoder_attention_heads) )
			if decoder_head_mask is None:
						lowerCAmelCase__									=							np.ones((config.decoder_layers, config.decoder_attention_heads) )
			if cross_attn_head_mask is None:
						lowerCAmelCase__									=							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 				A       :





	def __init__(   self     :    Any ,							__magic_name__     :    str ,							__magic_name__     :    Dict=13 ,							__magic_name__     :    Union[str, Any]=7 ,							__magic_name__     :    Any=True ,							__magic_name__     :    List[str]=False ,							__magic_name__     :    int=99 ,							__magic_name__     :    Optional[int]=16 ,							__magic_name__     :    Tuple=2 ,							__magic_name__     :    int=4 ,							__magic_name__     :    Optional[Any]=4 ,							__magic_name__     :    Optional[Any]="gelu" ,							__magic_name__     :    Optional[Any]=0.1 ,							__magic_name__     :    Any=0.1 ,							__magic_name__     :    Optional[int]=32 ,							__magic_name__     :    Tuple=2 ,							__magic_name__     :    Dict=1 ,							__magic_name__     :    Optional[Any]=0 ,							__magic_name__     :    Tuple=0.02 ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							parent
				lowerCAmelCase__									=							batch_size
				lowerCAmelCase__									=							seq_length
				lowerCAmelCase__									=							is_training
				lowerCAmelCase__									=							use_labels
				lowerCAmelCase__									=							vocab_size
				lowerCAmelCase__									=							hidden_size
				lowerCAmelCase__									=							num_hidden_layers
				lowerCAmelCase__									=							num_attention_heads
				lowerCAmelCase__									=							intermediate_size
				lowerCAmelCase__									=							hidden_act
				lowerCAmelCase__									=							hidden_dropout_prob
				lowerCAmelCase__									=							attention_probs_dropout_prob
				lowerCAmelCase__									=							max_position_embeddings
				lowerCAmelCase__									=							eos_token_id
				lowerCAmelCase__									=							pad_token_id
				lowerCAmelCase__									=							bos_token_id
				lowerCAmelCase__									=							initializer_range





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,							self.vocab_size							) ,							3 ,							self.vocab_size							)
				lowerCAmelCase__									=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,							dtype=np.intaa							)) ,							-1							)

				lowerCAmelCase__									=							shift_tokens_right(__magic_name__ ,							1 ,							2							)

				lowerCAmelCase__									=							MarianConfig(
				    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=__magic_name__ ,							)
				lowerCAmelCase__									=							prepare_marian_inputs_dict(__magic_name__ ,							__magic_name__ ,							__magic_name__							)
				return config, inputs_dict





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.prepare_config_and_inputs()
				return config, inputs_dict





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    Optional[Any] ,							__magic_name__     :    Dict ,							__magic_name__     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							20
				lowerCAmelCase__									=							model_class_name(__magic_name__							)

				lowerCAmelCase__									=							model.encode(inputs_dict["input_ids"]							)

				lowerCAmelCase__		,lowerCAmelCase__									=							(
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				lowerCAmelCase__									=							model.init_cache(decoder_input_ids.shape[0] ,							__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,							dtype="i4"							)

				lowerCAmelCase__									=							jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :] ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,							)
				lowerCAmelCase__									=							model.decode(
				    decoder_input_ids[:, :-1] ,							__magic_name__ ,							decoder_attention_mask=__magic_name__ ,							past_key_values=__magic_name__ ,							decoder_position_ids=__magic_name__ ,							)

				lowerCAmelCase__									=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,							dtype="i4"							)
				lowerCAmelCase__									=							model.decode(
				    decoder_input_ids[:, -1:] ,							__magic_name__ ,							decoder_attention_mask=__magic_name__ ,							past_key_values=outputs_cache.past_key_values ,							decoder_position_ids=__magic_name__ ,							)

				lowerCAmelCase__									=							model.decode(__magic_name__ ,							__magic_name__							)

				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Dict ,							__magic_name__     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							20
				lowerCAmelCase__									=							model_class_name(__magic_name__							)

				lowerCAmelCase__									=							model.encode(inputs_dict["input_ids"]							)

				lowerCAmelCase__		,lowerCAmelCase__									=							(
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

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

				lowerCAmelCase__									=							model.init_cache(decoder_input_ids.shape[0] ,							__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :] ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,							)

				lowerCAmelCase__									=							model.decode(
				    decoder_input_ids[:, :-1] ,							__magic_name__ ,							decoder_attention_mask=__magic_name__ ,							past_key_values=__magic_name__ ,							decoder_position_ids=__magic_name__ ,							)
				lowerCAmelCase__									=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,							dtype="i4"							)
				lowerCAmelCase__									=							model.decode(
				    decoder_input_ids[:, -1:] ,							__magic_name__ ,							past_key_values=outputs_cache.past_key_values ,							decoder_attention_mask=__magic_name__ ,							decoder_position_ids=__magic_name__ ,							)

				lowerCAmelCase__									=							model.decode(__magic_name__ ,							__magic_name__ ,							decoder_attention_mask=__magic_name__							)

				lowerCAmelCase__									=							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 				A       (		SCREAMING_SNAKE_CASE__					, unittest.TestCase					, SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Optional[Any]				       =       True
	snake_case__ :str				       =       (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
	snake_case__ :int				       =       (FlaxMarianMTModel,) if is_flax_available() else ()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							FlaxMarianModelTester(self							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(__magic_name__ ,							__magic_name__ ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							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(__magic_name__ ,							__magic_name__ ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__							):
										lowerCAmelCase__									=							self._prepare_for_class(__magic_name__ ,							__magic_name__							)
										lowerCAmelCase__									=							model_class(__magic_name__							)

										@jax.jit
										def encode_jitted(__magic_name__     :    Dict ,							__magic_name__     :    List[str]=None ,							**__magic_name__     :    Any							):
													return model.encode(input_ids=__magic_name__ ,							attention_mask=__magic_name__							)

										with self.subTest("JIT Enabled"							):
													lowerCAmelCase__									=							encode_jitted(**__magic_name__							).to_tuple()

										with self.subTest("JIT Disabled"							):
													with jax.disable_jit():
																lowerCAmelCase__									=							encode_jitted(**__magic_name__							).to_tuple()

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__							):
										lowerCAmelCase__									=							model_class(__magic_name__							)
										lowerCAmelCase__									=							model.encode(inputs_dict["input_ids"] ,							inputs_dict["attention_mask"]							)

										lowerCAmelCase__									=							{
										    "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(__magic_name__     :    Optional[int] ,							__magic_name__     :    Optional[Any] ,							__magic_name__     :    List[Any]							):
													return model.decode(
													    decoder_input_ids=__magic_name__ ,							decoder_attention_mask=__magic_name__ ,							encoder_outputs=__magic_name__ ,							)

										with self.subTest("JIT Enabled"							):
													lowerCAmelCase__									=							decode_jitted(**__magic_name__							).to_tuple()

										with self.subTest("JIT Disabled"							):
													with jax.disable_jit():
																lowerCAmelCase__									=							decode_jitted(**__magic_name__							).to_tuple()

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







	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				for model_class_name in self.all_model_classes:
							lowerCAmelCase__									=							model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"							)
							# FlaxMarianForSequenceClassification expects eos token in input_ids
							lowerCAmelCase__									=							np.ones((1, 1)							) * model.config.eos_token_id
							lowerCAmelCase__									=							model(__magic_name__							)
							self.assertIsNotNone(__magic_name__							)






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		unittest.TestCase      ):
	snake_case__ :str				       =       None
	snake_case__ :Tuple				       =       None





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
				return cls





	@cached_property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				return MarianTokenizer.from_pretrained(self.model_name							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				return self.tokenizer.eos_token_id





	@cached_property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							FlaxMarianMTModel.from_pretrained(self.model_name							)
				self.assertEqual(model.config.decoder_start_token_id ,							model.config.pad_token_id							)
				return model





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							**__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.translate_src_text(**__magic_name__							)
				self.assertListEqual(self.expected_text ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							**__magic_name__     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.tokenizer(self.src_text ,							padding=__magic_name__ ,							return_tensors="np" ,							**__magic_name__							)
				lowerCAmelCase__									=							self.model.generate(
				    model_inputs.input_ids ,							attention_mask=model_inputs.attention_mask ,							num_beams=2 ,							max_length=128 ,							).sequences
				lowerCAmelCase__									=							self.tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__							)
				return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :List[Any]				       =       'en'
	snake_case__ :Union[str, Any]				       =       'fr'
	snake_case__ :Optional[Any]				       =       [
	    'I am a small frog.',
	    'Now I can forget the 100 words of german that I know.',
	]
	snake_case__ :Optional[Any]				       =       [
	    'Je suis une petite grenouille.',
	    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
	]





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Optional[Any]				       =       'fr'
	snake_case__ :Optional[Any]				       =       'en'
	snake_case__ :Union[str, Any]				       =       [
	    'Donnez moi le micro.',
	    'Tom et Mary étaient assis à une table.',  # Accents
	]
	snake_case__ :Optional[int]				       =       [
	    'Give me the microphone.',
	    'Tom and Mary were sitting at a table.',
	]





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Optional[Any]				       =       'mt'
	snake_case__ :Optional[int]				       =       'en'
	snake_case__ :int				       =       ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
	snake_case__ :Any				       =       ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Optional[Any]				       =       'en'
	snake_case__ :Tuple				       =       'de'
	snake_case__ :List[str]				       =       [
	    'I am a small frog.',
	    'Now I can forget the 100 words of german that I know.',
	    'Tom asked his teacher for advice.',
	    'That\'s how I would do it.',
	    'Tom really admired Mary\'s courage.',
	    'Turn around and close your eyes.',
	]
	snake_case__ :Dict				       =       [
	    'Ich bin ein kleiner Frosch.',
	    'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
	    'Tom bat seinen Lehrer um Rat.',
	    'So würde ich das machen.',
	    'Tom bewunderte Marias Mut wirklich.',
	    'Drehen Sie sich um und schließen Sie die Augen.',
	]





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :int				       =       'en'
	snake_case__ :int				       =       'zh'
	snake_case__ :Optional[int]				       =       ['My name is Wolfgang and I live in Berlin']
	snake_case__ :Optional[int]				       =       ['我叫沃尔夫冈 我住在柏林']





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Any				       =       'ru'
	snake_case__ :str				       =       'fr'
	snake_case__ :Optional[Any]				       =       ['Он показал мне рукопись своей новой пьесы.']
	snake_case__ :Optional[int]				       =       ['Il m\'a montré le manuscrit de sa nouvelle pièce.']





	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Optional[int]				       =       'en'
	snake_case__ :Optional[Any]				       =       'ROMANCE'
	snake_case__ :Any				       =       [
	    '>>fr<< Don\'t spend so much time watching TV.',
	    '>>pt<< Your message has been sent.',
	    '>>es<< He\'s two years older than me.',
	]
	snake_case__ :Dict				       =       [
	    'Ne passez pas autant de temps à regarder la télé.',
	    'A sua mensagem foi enviada.',
	    'Es dos años más viejo que yo.',
	]





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


				"""simple docstring"""

				self._assert_generated_batch_equal_expected()







'''simple docstring'''

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     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

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

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

				return img_augs






class 				A       :





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


				"""simple docstring"""

				lowerCAmelCase__									=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,							cfg.INPUT.MAX_SIZE_TEST							)
				lowerCAmelCase__									=							cfg.INPUT.FORMAT
				lowerCAmelCase__									=							cfg.SIZE_DIVISIBILITY
				lowerCAmelCase__									=							cfg.PAD_VALUE
				lowerCAmelCase__									=							cfg.INPUT.MAX_SIZE_TEST
				lowerCAmelCase__									=							cfg.MODEL.DEVICE
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							lambda __magic_name__							: (x - self.pixel_mean) / self.pixel_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

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

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







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							input_format=self.input_format							)							)
													    .to(self.device							)
													    .float() ,							)
            # resize smallest edge
							lowerCAmelCase__									=							torch.tensor([im.shape[:2] for im in images]							)
							lowerCAmelCase__									=							self.aug(__magic_name__							)
							# 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
							lowerCAmelCase__									=							[self.normalizer(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

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







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



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





