
"""simple docstring"""




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor

class 		a__						:
   def __init__(			self	,    _a	,    _a=13	,    _a=3	,    _a=True	,    _a=True	,    _a=0.1	,    _a=0.1	,    _a=224	,    _a=1_000	,    _a=[3, 3, 6, 4]	,    _a=[48, 56, 112, 220]	,    ):
          lowercase    :				Union[str, Any]       		=							parent
          lowercase    :				List[Any]       		=							batch_size
          lowercase    :				Any       		=							num_channels
          lowercase    :				List[str]       		=							is_training
          lowercase    :				Optional[Any]       		=							use_labels
          lowercase    :				Optional[int]       		=							hidden_dropout_prob
          lowercase    :				List[Any]       		=							attention_probs_dropout_prob
          lowercase    :				int       		=							num_labels
          lowercase    :				Any       		=							image_size
          lowercase    :				Any       		=							layer_depths
          lowercase    :				Union[str, Any]       		=							embed_dims
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          lowercase    :				Optional[Any]       		=							None
          if self.use_labels:
                 lowercase    :				int       		=							ids_tensor([self.batch_size]	,    self.num_labels				)

          lowercase    :				str       		=							self.get_config()

          return config, pixel_values, labels
   def 			__magic_name__			(			self				):
          return SwiftFormerConfig(
              depths=self.layer_depths	,    embed_dims=self.embed_dims	,    mlp_ratio=4	,    downsamples=[True, True, True, True]	,    hidden_act="gelu"	,    num_labels=self.num_labels	,    down_patch_size=3	,    down_stride=2	,    down_pad=1	,    drop_rate=0.0	,    drop_path_rate=0.0	,    use_layer_scale=__UpperCamelCase	,    layer_scale_init_value=1E-5	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							SwiftFormerModel(config=__UpperCamelCase				)
          model.to(__UpperCamelCase				)
          model.eval()
          lowercase    :				int       		=							model(__UpperCamelCase				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.embed_dims[-1], 7, 7)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[Any]       		=							self.num_labels
          lowercase    :				str       		=							SwiftFormerForImageClassification(__UpperCamelCase				)
          model.to(__UpperCamelCase				)
          model.eval()
          lowercase    :				Tuple       		=							model(__UpperCamelCase	,    labels=__UpperCamelCase				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)				)

          lowercase    :				Optional[int]       		=							SwiftFormerForImageClassification(__UpperCamelCase				)
          model.to(__UpperCamelCase				)
          model.eval()

          lowercase    :				Any       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
          lowercase    :				str       		=							model(__UpperCamelCase				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)				)

   def 			__magic_name__			(			self				):
          (lowercase)    :				List[str]       		=							self.prepare_config_and_inputs()
          lowercase    :				List[str]       		=							{'pixel_values': pixel_values}
          return config, inputs_dict


@require_torch
class 		a__						(      lowercase_,			lowercase_,			unittest.TestCase						):
   __lowerCAmelCase            =				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   __lowerCAmelCase            =				(
       {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							SwiftFormerModelTester(self				)
          lowercase    :				Optional[int]       		=							ConfigTester(
              self	,    config_class=__UpperCamelCase	,    has_text_modality=__UpperCamelCase	,    hidden_size=37	,    num_attention_heads=12	,    num_hidden_layers=12	,    )
   def 			__magic_name__			(			self				):
          self.config_tester.run_common_tests()
   @unittest.skip(reason="SwiftFormer does not use inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Any       		=							model_class(__UpperCamelCase				)
                 lowercase    :				Union[str, Any]       		=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__UpperCamelCase	,    nn.Linear				)				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Any       		=							model_class(__UpperCamelCase				)
                 lowercase    :				Optional[int]       		=							inspect.signature(model.forward				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowercase    :				Tuple       		=							[*signature.parameters.keys()]

                 lowercase    :				Any       		=							['pixel_values']
                 self.assertListEqual(arg_names[:1]	,    __UpperCamelCase				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase				)
   @slow
   def 			__magic_name__			(			self				):


          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase    :				str       		=							SwiftFormerModel.from_pretrained(__UpperCamelCase				)
                 self.assertIsNotNone(__UpperCamelCase				)
   @unittest.skip(reason="SwiftFormer does not output attentions"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):


          def check_hidden_states_output(_a	,    _a	,    _a				):
                 lowercase    :				List[str]       		=							model_class(__UpperCamelCase				)
                 model.to(__UpperCamelCase				)
                 model.eval()

                 with torch.no_grad():
                        lowercase    :				str       		=							model(**self._prepare_for_class(__UpperCamelCase	,    __UpperCamelCase				)				)

                 lowercase    :				Dict       		=							outputs.hidden_states

                 lowercase    :				Union[str, Any]       		=							8
                 self.assertEqual(len(__UpperCamelCase				)	,    __UpperCamelCase				)  # TODO

                 # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                 # with the width and height being successively divided by 2, after every 2 blocks
                 for i in range(len(__UpperCamelCase				)				):
                        self.assertEqual(
                            hidden_states[i].shape	,    torch.Size(
                                [
                                    self.model_tester.batch_size,
                                    self.model_tester.embed_dims[i // 2],
                                    (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                    (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                ]				)	,    )

          lowercase    :				Any       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Any       		=							True
                 check_hidden_states_output(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase				)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase    :				Dict       		=							True

                 check_hidden_states_output(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase				)
   def 			__magic_name__			(			self				):


          def _config_zero_init(_a				):
                 lowercase    :				Optional[Any]       		=							copy.deepcopy(__UpperCamelCase				)
                 for key in configs_no_init.__dict__.keys():
                        if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                               setattr(__UpperCamelCase	,    __UpperCamelCase	,    1E-10				)
                        if isinstance(getattr(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase				)	,    __UpperCamelCase				):
                               lowercase    :				Optional[Any]       		=							_config_zero_init(getattr(__UpperCamelCase	,    __UpperCamelCase				)				)
                               setattr(__UpperCamelCase	,    __UpperCamelCase	,    __UpperCamelCase				)
                 return configs_no_init

          lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				Union[str, Any]       		=							_config_zero_init(__UpperCamelCase				)
          for model_class in self.all_model_classes:
                 lowercase    :				Tuple       		=							model_class(config=__UpperCamelCase				)
                 for name, param in model.named_parameters():
                        if param.requires_grad:
                               self.assertIn(
                                   ((param.data.mean() * 1E9) / 1E9).round().item()	,    [0.0, 1.0]	,    msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,    )

   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
   def 			__magic_name__			(			self				):
          pass


def   __magic_name__	(      ) ->				Optional[Any]:
       lowercase    :				List[Any]       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_torch
@require_vision
class 		a__						(      unittest.TestCase						):
   @cached_property
   def 			__magic_name__			(			self				):
          return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"				) if is_vision_available() else None

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"				).to(__UpperCamelCase				)

          lowercase    :				List[Any]       		=							self.default_image_processor
          lowercase    :				Dict       		=							prepare_img()
          lowercase    :				int       		=							image_processor(images=__UpperCamelCase	,    return_tensors="pt"				).to(__UpperCamelCase				)

          # forward pass
          with torch.no_grad():
                 lowercase    :				Union[str, Any]       		=							model(**__UpperCamelCase				)

          # verify the logits
          lowercase    :				Tuple       		=							torch.Size((1, 1_000)				)
          self.assertEqual(outputs.logits.shape	,    __UpperCamelCase				)

          lowercase    :				Optional[Any]       		=							torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]				).to(__UpperCamelCase				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]	,    __UpperCamelCase	,    atol=1E-4				)				)



"""simple docstring"""







import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf

       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			enum.Enum  ):
   lowercase							=  0
   lowercase							=  1







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'generated'
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        super().__init__(*__UpperCamelCase     ,**__UpperCamelCase			)

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	{}
        if truncation is not None:
             lowercase_	:				int													=	truncation

        lowercase_	:				Dict													=	generate_kwargs

        lowercase_	:				List[Any]													=	{}
        if return_tensors is not None and return_type is None:
             lowercase_	:				Union[str, Any]													=	ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
             lowercase_	:				str													=	return_type

        if clean_up_tokenization_spaces is not None:
             lowercase_	:				Dict													=	clean_up_tokenization_spaces

        if stop_sequence is not None:
             lowercase_	:				Union[str, Any]													=	self.tokenizer.encode(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase			)
             if len(__UpperCamelCase			) > 1:
                  warnings.warn(
                      'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                      ' the stop sequence will be used as the stop sequence string in the interim.'			)
             lowercase_	:				Optional[int]													=	stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        return True
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.model.config.prefix if self.model.config.prefix is not None else ''
        if isinstance(args[0]     ,__UpperCamelCase			):
             if self.tokenizer.pad_token_id is None:
                  raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'			)
             lowercase_	:				str													=	([prefix + arg for arg in args[0]],)
             lowercase_	:				Union[str, Any]													=	True

        elif isinstance(args[0]     ,__UpperCamelCase			):
             lowercase_	:				Union[str, Any]													=	(prefix + args[0],)
             lowercase_	:				Union[str, Any]													=	False
        else:
             raise ValueError(
                 f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''			)
        lowercase_	:				List[Any]													=	self.tokenizer(*__UpperCamelCase     ,padding=__UpperCamelCase     ,truncation=__UpperCamelCase     ,return_tensors=self.framework			)
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
             del inputs["token_type_ids"]
        return inputs
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().__call__(*__UpperCamelCase     ,**__UpperCamelCase			)
        if (
            isinstance(args[0]     ,__UpperCamelCase			)
            and all(isinstance(__UpperCamelCase     ,__UpperCamelCase			) for el in args[0]			)
            and all(len(__UpperCamelCase			) == 1 for res in result			)
        ):
             return [res[0] for res in result]
        return result
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,**__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Any													=	self._parse_and_tokenize(__UpperCamelCase     ,truncation=__UpperCamelCase     ,**__UpperCamelCase			)
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        if self.framework == "pt":
             lowercase_							,  lowercase_	:				Optional[int]													=	model_inputs['input_ids'].shape
        elif self.framework == "tf":
             lowercase_							,  lowercase_	:				Union[str, Any]													=	tf.shape(model_inputs['input_ids']			).numpy()

        lowercase_	:				str													=	generate_kwargs.get('min_length'     ,self.model.config.min_length			)
        lowercase_	:				List[Any]													=	generate_kwargs.get('max_length'     ,self.model.config.max_length			)
        self.check_inputs(__UpperCamelCase     ,generate_kwargs['min_length']     ,generate_kwargs['max_length']			)
        lowercase_	:				Tuple													=	self.model.generate(**__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				str													=	output_ids.shape[0]
        if self.framework == "pt":
             lowercase_	:				List[Any]													=	output_ids.reshape(__UpperCamelCase     ,out_b // in_b     ,*output_ids.shape[1:]			)
        elif self.framework == "tf":
             lowercase_	:				List[Any]													=	tf.reshape(__UpperCamelCase     ,(in_b, out_b // in_b, *output_ids.shape[1:])			)
        return {"output_ids": output_ids}







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=ReturnType.TEXT     ,__UpperCamelCase=False			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	[]
        for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                  lowercase_	:				List[Any]													=	{f'''{self.return_name}_token_ids''': output_ids}
             elif return_type == ReturnType.TEXT:
                  lowercase_	:				str													=	{
                      f'''{self.return_name}_text''': self.tokenizer.decode(
                          __UpperCamelCase     ,skip_special_tokens=__UpperCamelCase     ,clean_up_tokenization_spaces=__UpperCamelCase     ,)
                  }
             records.append(__UpperCamelCase			)
        return records







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'summary'
   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        return super().__call__(*__UpperCamelCase     ,**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      bool:


        '''simple docstring'''


        if max_length < min_length:
             logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''			)

        if input_length < max_length:
             logger.warning(
                 f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
                 'a summarization task, where outputs shorter than the input are typically wanted, you might '
                 f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''			)







@add_end_docstrings(lowercase_  )
class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  'translation'
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        if input_length > 0.9 * max_length:
             logger.warning(
                 f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
                 'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'			)
        return True
   def 			_UpperCAmelCase   (			self     ,*__UpperCamelCase     ,__UpperCamelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,__UpperCamelCase=None     ,__UpperCamelCase=None			)	->      int:


        '''simple docstring'''


        if getattr(self.tokenizer     ,'_build_translation_inputs'     ,__UpperCamelCase			):
             return self.tokenizer._build_translation_inputs(
                 *__UpperCamelCase     ,return_tensors=self.framework     ,truncation=__UpperCamelCase     ,src_lang=__UpperCamelCase     ,tgt_lang=__UpperCamelCase			)
        else:
             return super()._parse_and_tokenize(*__UpperCamelCase     ,truncation=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_	:				int													=	super()._sanitize_parameters(**__UpperCamelCase			)
        if src_lang is not None:
             lowercase_	:				str													=	src_lang
        if tgt_lang is not None:
             lowercase_	:				Optional[Any]													=	tgt_lang
        if src_lang is None and tgt_lang is None:
             # Backward compatibility, direct arguments use is preferred.
             lowercase_	:				Tuple													=	kwargs.get('task'     ,self.task			)
             lowercase_	:				List[str]													=	task.split('_'			)
             if task and len(__UpperCamelCase			) == 4:
                  # translation, XX, to YY
                  lowercase_	:				Union[str, Any]													=	items[1]
                  lowercase_	:				Tuple													=	items[3]
        return preprocess_params, forward_params, postprocess_params







   def __call__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        return super().__call__(*__UpperCamelCase     ,**__UpperCamelCase			)

