






'''simple docstring'''
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							Dict   , __lowercase		:							Optional[Any]   , __lowercase		:							Optional[int]=99   , __lowercase		:							str=13   , __lowercase		:							int=7   , __lowercase		:							Any=9   , __lowercase		:							Optional[int]=True   , __lowercase		:							Any=True   , __lowercase		:							Tuple=False   , __lowercase		:							Optional[Any]=32   , __lowercase		:							List[str]=5   , __lowercase		:							int=4   , __lowercase		:							List[str]=37   , __lowercase		:							Tuple=8   , __lowercase		:							Any=0.1   , __lowercase		:							Tuple=0.002   , __lowercase		:							List[Any]=1   , __lowercase		:							Union[str, Any]=0   , __lowercase		:							int=0   , __lowercase		:							str=None   , __lowercase		:							List[str]=None   , )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			str													=parent
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=batch_size
       SCREAMING_SNAKE_CASE__       :			Tuple													=encoder_seq_length
       SCREAMING_SNAKE_CASE__       :			List[Any]													=decoder_seq_length
       # For common tests
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.decoder_seq_length
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=is_training
       SCREAMING_SNAKE_CASE__       :			str													=use_attention_mask
       SCREAMING_SNAKE_CASE__       :			Tuple													=use_labels
       SCREAMING_SNAKE_CASE__       :			List[Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=hidden_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			str													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=d_ff
       SCREAMING_SNAKE_CASE__       :			int													=relative_attention_num_buckets
       SCREAMING_SNAKE_CASE__       :			Dict													=dropout_rate
       SCREAMING_SNAKE_CASE__       :			Any													=initializer_factor
       SCREAMING_SNAKE_CASE__       :			int													=eos_token_id
       SCREAMING_SNAKE_CASE__       :			Dict													=pad_token_id
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=decoder_start_token_id
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=None
       SCREAMING_SNAKE_CASE__       :			Any													=decoder_layers




 def        __magic_name__		( self		:							Optional[Any] )		->  Dict:
       return TaConfig.from_pretrained('''google/umt5-base''' )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							Any   , __lowercase		:							Union[str, Any]   , __lowercase		:							Tuple   , __lowercase		:							Union[str, Any]=None   , __lowercase		:							Optional[int]=None   , __lowercase		:							Tuple=None   , __lowercase		:							Union[str, Any]=None   , __lowercase		:							List[Any]=None   , )		->  Any:
       if attention_mask is None:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=input_ids.ne(config.pad_token_id )
       if decoder_attention_mask is None:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=decoder_input_ids.ne(config.pad_token_id )
       if head_mask is None:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.ones(config.num_hidden_layers   , config.num_attention_heads   , device=__lowercase )
       if decoder_head_mask is None:
             SCREAMING_SNAKE_CASE__       :			Any													=torch.ones(config.num_decoder_layers   , config.num_attention_heads   , device=__lowercase )
       if cross_attn_head_mask is None:
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.ones(
                 config.num_decoder_layers   , config.num_attention_heads   , device=__lowercase )
       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,
       }




 def        __magic_name__		( self		:							Any )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=ids_tensor([self.batch_size, self.encoder_seq_length]   , self.vocab_size )
       SCREAMING_SNAKE_CASE__       :			Any													=ids_tensor([self.batch_size, self.decoder_seq_length]   , self.vocab_size )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for NllbMoe the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       SCREAMING_SNAKE_CASE__       :			int													=input_ids.clamp(self.pad_token_id + 1 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=decoder_input_ids.clamp(self.pad_token_id + 1 )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=self.get_config()
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=config.num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.prepare_inputs_dict(__lowercase   , __lowercase   , __lowercase )
       return config, input_dict




 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.prepare_config_and_inputs()
       return config, inputs_dict




 def        __magic_name__		( self		:							Tuple )		->  Tuple:
       return TaConfig(
           vocab_size=1_66   , d_model=self.hidden_size   , d_ff=self.d_ff   , d_kv=self.hidden_size // self.num_attention_heads   , num_layers=self.num_hidden_layers   , num_decoder_layers=self.decoder_layers   , num_heads=self.num_attention_heads   , relative_attention_num_buckets=self.relative_attention_num_buckets   , dropout_rate=self.dropout_rate   , initializer_factor=self.initializer_factor   , eos_token_id=self.eos_token_id   , bos_token_id=self.pad_token_id   , pad_token_id=self.pad_token_id   , decoder_start_token_id=self.decoder_start_token_id   , )




 def        __magic_name__		( self		:							Any )		->  List[str]:
       return TaConfig(
           vocab_size=self.vocab_size   , d_model=self.hidden_size   , d_ff=self.d_ff   , d_kv=self.hidden_size // self.num_attention_heads   , num_layers=self.num_hidden_layers   , num_decoder_layers=self.decoder_layers   , num_heads=self.num_attention_heads   , relative_attention_num_buckets=self.relative_attention_num_buckets   , dropout_rate=self.dropout_rate   , initializer_factor=self.initializer_factor   , eos_token_id=self.eos_token_id   , bos_token_id=self.pad_token_id   , pad_token_id=self.pad_token_id   , decoder_start_token_id=self.decoder_start_token_id   , )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Any   , __lowercase		:							int   , __lowercase		:							Dict   , __lowercase		:							str   , __lowercase		:							List[Any]   , __lowercase		:							Optional[int]   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Dict													=UMTaModel(config=__lowercase )
       model.to(__lowercase )
       model.eval()
       SCREAMING_SNAKE_CASE__       :			Any													=model(
           input_ids=__lowercase   , decoder_input_ids=__lowercase   , attention_mask=__lowercase   , decoder_attention_mask=__lowercase   , )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=model(input_ids=__lowercase   , decoder_input_ids=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=result.last_hidden_state
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=result.past_key_values
       SCREAMING_SNAKE_CASE__       :			str													=result.encoder_last_hidden_state

       self.parent.assertEqual(encoder_output.size()   , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
       self.parent.assertEqual(decoder_output.size()   , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
       # There should be `num_layers` key value embeddings stored in decoder_past
       self.parent.assertEqual(len(__lowercase )   , config.num_layers )
       # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
       self.parent.assertEqual(len(decoder_past[0] )   , 4 )




 def        __magic_name__		( self		:							int   , __lowercase		:							int   , __lowercase		:							List[str]   , __lowercase		:							Tuple   , __lowercase		:							List[Any]   , __lowercase		:							Tuple   , __lowercase		:							Optional[int]   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=UMTaModel(config=__lowercase ).get_decoder().to(__lowercase ).eval()
       # first forward pass
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=model(__lowercase   , use_cache=__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=model(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=model(__lowercase   , use_cache=__lowercase )

       self.parent.assertTrue(len(__lowercase ) == len(__lowercase ) )
       self.parent.assertTrue(len(__lowercase ) == len(__lowercase ) + 1 )

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=ids_tensor((self.batch_size, 1)   , config.vocab_size )

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.cat([input_ids, next_tokens]   , dim=-1 )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=model(__lowercase )['''last_hidden_state''']
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=model(__lowercase   , past_key_values=__lowercase )['''last_hidden_state''']

       # select random slice
       SCREAMING_SNAKE_CASE__       :			Tuple													=ids_tensor((1,)   , output_from_past.shape[-1] ).item()
       SCREAMING_SNAKE_CASE__       :			str													=output_from_no_past[:, -1, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(__lowercase   , __lowercase   , atol=1e-3 ) )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							int   , __lowercase		:							str   , )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=UMTaModel(config=__lowercase ).to(__lowercase ).half().eval()
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model(**__lowercase )['''last_hidden_state''']
       self.parent.assertFalse(torch.isnan(__lowercase ).any().item() )






@require_torch
class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 snake_case_												=							(UMTaForConditionalGeneration,) if is_torch_available() else ()
 snake_case_												=							(
     {
         """conversational""": UMTaForConditionalGeneration,
         """feature-extraction""": UMTaModel,
         """summarization""": UMTaForConditionalGeneration,
         """text2text-generation""": UMTaForConditionalGeneration,
         """translation""": UMTaForConditionalGeneration,
         """question-answering""": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 snake_case_												=							True
 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							True
 snake_case_												=							True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 snake_case_												=							[0.8, 0.9]




 def        __magic_name__		( self		:							Union[str, Any] )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=UMTaModelTester(self )




 @unittest.skip('''Test has a segmentation fault on torch 1.8.0''' )
 def        __magic_name__		( self		:							Optional[int] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=UMTaModel(config_and_inputs[0] ).to(__lowercase )
       with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
                 __lowercase   , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])   , F"{tmpdirname}/t5_test.onnx"   , export_params=__lowercase   , opset_version=9   , input_names=['''input_ids''', '''decoder_input_ids''']   , )




 @unittest.skipIf(torch_device == '''cpu'''   , '''Cant do half precision''' )
 def        __magic_name__		( self		:							Optional[Any] )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*__lowercase )




 def        __magic_name__		( self		:							int )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Tuple													=['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__       :			int													=config_and_inputs[0]
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=UMTaForConditionalGeneration(__lowercase ).eval()
       model.to(__lowercase )

       SCREAMING_SNAKE_CASE__       :			int													={
           '''head_mask''': torch.zeros(config.num_layers   , config.num_heads   , device=__lowercase ),
           '''decoder_head_mask''': torch.zeros(config.num_decoder_layers   , config.num_heads   , device=__lowercase ),
           '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers   , config.num_heads   , device=__lowercase ),
       }

       for attn_name, (name, mask) in zip(__lowercase   , head_masking.items() ):
             SCREAMING_SNAKE_CASE__       :			str													={name: mask}
             # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
             if name == "head_mask":
                   SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.ones(
                       config.num_decoder_layers   , config.num_heads   , device=__lowercase )

             SCREAMING_SNAKE_CASE__       :			List[Any]													=model.generate(
                 config_and_inputs[1]['''input_ids''']   , num_beams=1   , max_length=3   , output_attentions=__lowercase   , return_dict_in_generate=__lowercase   , **__lowercase   , )
             # We check the state of decoder_attentions and cross_attentions just from the last step
             SCREAMING_SNAKE_CASE__       :			Tuple													=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
             self.assertEqual(sum([w.sum().item() for w in attn_weights] )   , 0.0 )




 @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.''' )
 def        __magic_name__		( self		:							Union[str, Any] )		->  Union[str, Any]:
       pass






@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 @slow
 @unittest.skip(
     '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged''' )
 def        __magic_name__		( self		:							Dict )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''   , return_dict=__lowercase ).to(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=AutoTokenizer.from_pretrained('''google/umt5-small'''   , use_fast=__lowercase   , legacy=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=[
           '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
           '''No se como puedo <extra_id_0>.''',
           '''This is the reason why we <extra_id_0> them.''',
           '''The <extra_id_0> walks in <extra_id_1>, seats''',
           '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
       ]
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=tokenizer(__lowercase   , return_tensors='''pt'''   , padding=__lowercase ).input_ids
       # fmt: off
       SCREAMING_SNAKE_CASE__       :			int													=torch.tensor(
           [
               [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
           ] )
       # fmt: on
       torch.testing.assert_allclose(__lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			List[str]													=model.generate(input_ids.to(__lowercase ) )
       SCREAMING_SNAKE_CASE__       :			str													=[
           '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
           '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
       ]
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tokenizer.batch_decode(__lowercase )
       self.assertEqual(__lowercase   , __lowercase )







'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""pixel_values"""]




 def __init__( self		:							List[str]   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							bool = True   , __lowercase		:							Union[int, float] = 1 / 2_55   , __lowercase		:							bool = True   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , **__lowercase		:							List[Any]   , )		->  None:
       super().__init__(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=size if size is not None else {'''shortest_edge''': 2_56}
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			int													=do_resize
       SCREAMING_SNAKE_CASE__       :			Dict													=size
       SCREAMING_SNAKE_CASE__       :			List[str]													=resample
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_center_crop
       SCREAMING_SNAKE_CASE__       :			str													=crop_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_rescale
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__       :			Any													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[Any]   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
       return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							int   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Any   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
       if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
       return center_crop(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Tuple )		->  np.ndarray:
       return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							np.ndarray   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							str   , )		->  np.ndarray:
       return normalize(__lowercase   , mean=__lowercase   , std=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__       :			int													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__       :			int													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__       :			Any													=image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__       :			str													=make_list_of_images(__lowercase )

       if not valid_images(__lowercase ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

       if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.''' )

       if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

       if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__       :			Dict													=[to_numpy_array(__lowercase ) for image in images]

       if do_resize:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[self.resize(image=__lowercase   , size=__lowercase   , resample=__lowercase ) for image in images]

       if do_center_crop:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.center_crop(image=__lowercase   , size=__lowercase ) for image in images]

       if do_rescale:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.rescale(image=__lowercase   , scale=__lowercase ) for image in images]

       if do_normalize:
             SCREAMING_SNAKE_CASE__       :			Tuple													=[self.normalize(image=__lowercase   , mean=__lowercase   , std=__lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			str													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			Any													={'''pixel_values''': images}
       return BatchFeature(data=__lowercase   , tensor_type=__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Tuple   , __lowercase		:							List[Tuple] = None )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(__lowercase ) != len(__lowercase ):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

             if is_torch_tensor(__lowercase ):
                   SCREAMING_SNAKE_CASE__       :			int													=target_sizes.numpy()

             SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

             for idx in range(len(__lowercase ) ):
                   SCREAMING_SNAKE_CASE__       :			Any													=torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0 )   , size=target_sizes[idx]   , mode='''bilinear'''   , align_corners=__lowercase )
                   SCREAMING_SNAKE_CASE__       :			List[str]													=resized_logits[0].argmax(dim=0 )
                   semantic_segmentation.append(__lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			Any													=logits.argmax(dim=1 )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

       return semantic_segmentation