




import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      __SCREAMING_SNAKE_CASE			:      List[str]      			=False


if is_vision_available():
      from PIL import Image
else:






      class 	A	:
           """simple docstring"""

           @staticmethod
           def        __snake_case      (			*A__     :      Union[str, Any]		,     **A__     :      Optional[Any]	) ->				List[str]:
                 pass






@is_pipeline_test
@require_vision
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     _snake_case:		List[Any]   =MODEL_FOR_VISION_2_SEQ_MAPPING
     _snake_case:		Optional[Any]   =TF_MODEL_FOR_VISION_2_SEQ_MAPPING




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Any		,     A__     :      Union[str, Any]	) ->				Tuple:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model=A__		,     tokenizer=A__		,     image_processor=A__	)
           lowerCamelCase__					     = [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
               './tests/fixtures/tests_samples/COCO/000000039769.png',
           ]
           return pipe, examples




     def        __snake_case      (			self     :      List[str]		,     A__     :      int		,     A__     :      Tuple	) ->				Union[str, Any]:
           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(
               A__		,     [
                   [{'generated_text': ANY(A__	)}],
                   [{'generated_text': ANY(A__	)}],
               ]		,     )




     @require_tf
     def        __snake_case      (			self     :      Optional[int]	) ->				List[str]:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='hf-internal-testing/tiny-random-vit-gpt2'		,     framework='tf'	)
           lowerCamelCase__					     = './tests/fixtures/tests_samples/COCO/000000039769.png'

           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(
               A__		,     [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   },
               ]		,     )

           lowerCamelCase__					     = pipe([image, image]	)
           self.assertEqual(
               A__		,     [
                   [
                       {
                           'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                       }
                   ],
                   [
                       {
                           'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                       }
                   ],
               ]		,     )

           lowerCamelCase__					     = pipe(A__		,     max_new_tokens=1	)
           self.assertEqual(
               A__		,     [{'generated_text': 'growth'}]		,     )




     @require_torch
     def        __snake_case      (			self     :      int	) ->				Dict:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='hf-internal-testing/tiny-random-vit-gpt2'	)
           lowerCamelCase__					     = './tests/fixtures/tests_samples/COCO/000000039769.png'

           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(
               A__		,     [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   },
               ]		,     )

           lowerCamelCase__					     = pipe([image, image]	)
           self.assertEqual(
               A__		,     [
                   [
                       {
                           'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                       }
                   ],
                   [
                       {
                           'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                       }
                   ],
               ]		,     )




     @require_torch
     def        __snake_case      (			self     :      Union[str, Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'	)
           lowerCamelCase__					     = './tests/fixtures/tests_samples/COCO/000000039769.png'
           lowerCamelCase__					     = 'a photo of'

           lowerCamelCase__					     = pipe(A__		,     prompt=A__	)
           self.assertTrue(outputs[0]['generated_text'].startswith(A__	)	)




     @slow
     @require_torch
     def        __snake_case      (			self     :      Union[str, Any]	) ->				Optional[int]:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='ydshieh/vit-gpt2-coco-en'	)
           lowerCamelCase__					     = './tests/fixtures/tests_samples/COCO/000000039769.png'

           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(A__		,     [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)

           lowerCamelCase__					     = pipe([image, image]	)
           self.assertEqual(
               A__		,     [
                   [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                   [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
               ]		,     )




     @slow
     @require_torch
     def        __snake_case      (			self     :      Tuple	) ->				Tuple:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='Salesforce/blip-image-captioning-base'	)
           lowerCamelCase__					     = 'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
           lowerCamelCase__					     = Image.open(requests.get(A__		,     stream=A__	).raw	)

           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(A__		,     [{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]	)




     @slow
     @require_torch
     def        __snake_case      (			self     :      int	) ->				Any:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='microsoft/git-base-coco'	)
           lowerCamelCase__					     = 'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
           lowerCamelCase__					     = Image.open(requests.get(A__		,     stream=A__	).raw	)

           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(A__		,     [{'generated_text': 'a cartoon of a purple character.'}]	)




     @slow
     @require_torch
     def        __snake_case      (			self     :      List[str]	) ->				Union[str, Any]:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='Salesforce/blip-image-captioning-base'	)
           lowerCamelCase__					     = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
           lowerCamelCase__					     = Image.open(requests.get(A__		,     stream=A__	).raw	)

           lowerCamelCase__					     = 'a photography of'

           lowerCamelCase__					     = pipe(A__		,     prompt=A__	)
           self.assertEqual(A__		,     [{'generated_text': 'a photography of a volcano'}]	)

           with self.assertRaises(A__	):
                 lowerCamelCase__					     = pipe([image, image]		,     prompt=[prompt, prompt]	)




     @slow
     @require_torch
     def        __snake_case      (			self     :      Union[str, Any]	) ->				Tuple:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='microsoft/git-base-coco'	)
           lowerCamelCase__					     = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
           lowerCamelCase__					     = Image.open(requests.get(A__		,     stream=A__	).raw	)

           lowerCamelCase__					     = 'a photo of a'

           lowerCamelCase__					     = pipe(A__		,     prompt=A__	)
           self.assertEqual(A__		,     [{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]	)

           with self.assertRaises(A__	):
                 lowerCamelCase__					     = pipe([image, image]		,     prompt=[prompt, prompt]	)




     @unittest.skipIf(
         not is_torch_greater_or_equal_than_1_11		,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'	)
     @slow
     @require_torch
     def        __snake_case      (			self     :      str	) ->				int:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='google/pix2struct-ai2d-base'	)
           lowerCamelCase__					     = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
           lowerCamelCase__					     = Image.open(requests.get(A__		,     stream=A__	).raw	)

           lowerCamelCase__					     = 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

           lowerCamelCase__					     = pipe(A__		,     prompt=A__	)
           self.assertEqual(A__		,     [{'generated_text': 'ash cloud'}]	)

           with self.assertRaises(A__	):
                 lowerCamelCase__					     = pipe([image, image]		,     prompt=[prompt, prompt]	)




     @slow
     @require_tf
     def        __snake_case      (			self     :      str	) ->				Dict:
           lowerCamelCase__					     = pipeline('image-to-text'		,     model='ydshieh/vit-gpt2-coco-en'		,     framework='tf'	)
           lowerCamelCase__					     = './tests/fixtures/tests_samples/COCO/000000039769.png'

           lowerCamelCase__					     = pipe(A__	)
           self.assertEqual(A__		,     [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)

           lowerCamelCase__					     = pipe([image, image]	)
           self.assertEqual(
               A__		,     [
                   [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                   [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
               ]		,     )





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.config_class(**self.inputs_dict	)
           self.parent.assertTrue(hasattr(A__		,     'hidden_sizes'	)	)
           self.parent.assertTrue(hasattr(A__		,     'num_encoder_blocks'	)	)






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int=1_3		,     A__     :      Optional[Any]=6_4		,     A__     :      List[Any]=3		,     A__     :      Dict=4		,     A__     :      int=[2, 2, 2, 2]		,     A__     :      int=[8, 4, 2, 1]		,     A__     :      Any=[1_6, 3_2, 6_4, 1_2_8]		,     A__     :      List[str]=[1, 4, 8, 1_6]		,     A__     :      List[str]=False		,     A__     :      Optional[int]=True		,     A__     :      Any="gelu"		,     A__     :      List[str]=0.1		,     A__     :      Union[str, Any]=0.02		,     A__     :      Any=3		,     A__     :      Dict=None		,     ) ->				List[str]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = num_encoder_blocks
           lowerCamelCase__					     = sr_ratios
           lowerCamelCase__					     = depths
           lowerCamelCase__					     = hidden_sizes
           lowerCamelCase__					     = downsampling_rates
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = scope




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase__					     = None
           if self.use_labels:
                 lowerCamelCase__					     = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels	)

           lowerCamelCase__					     = PoolFormerConfig(
               image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     initializer_range=self.initializer_range		,     )

           return config, pixel_values, labels




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Optional[Any]	) ->				str:
           lowerCamelCase__					     = PoolFormerModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)
           lowerCamelCase__					     = lowerCamelCase__					     = self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)




     def        __snake_case      (			self     :      List[str]	) ->				Any:
           lowerCamelCase__					     = self.prepare_config_and_inputs()
           lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = config_and_inputs
           lowerCamelCase__					     = {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _snake_case:		Dict   =(
         {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _snake_case:		str   =False
     _snake_case:		Tuple   =False
     _snake_case:		Optional[int]   =False
     _snake_case:		Dict   =False
     _snake_case:		Tuple   =False




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = PoolFormerModelTester(self	)
           lowerCamelCase__					     = PoolFormerConfigTester(self		,     config_class=A__	)




     def        __snake_case      (			self     :      str	) ->				List[str]:
           self.config_tester.run_common_tests()




     def        __snake_case      (			self     :      int	) ->				List[str]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     @unittest.skip('PoolFormer does not use inputs_embeds'	)
     def        __snake_case      (			self     :      Dict	) ->				Tuple:
           pass




     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
     def        __snake_case      (			self     :      List[str]	) ->				str:
           pass




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           def check_hidden_states_output(A__     :      str		,     A__     :      List[Any]		,     A__     :      List[Any]	):
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase__					     = model(**self._prepare_for_class(A__		,     A__	)	)

                 lowerCamelCase__					     = outputs.hidden_states

                 lowerCamelCase__					     = self.model_tester.num_encoder_blocks
                 self.assertEqual(len(A__	)		,     A__	)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]	)		,     [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]		,     )

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = True
                 check_hidden_states_output(A__		,     A__		,     A__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase__					     = True

                 check_hidden_states_output(A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      Any	) ->				Tuple:
           if not self.model_tester.is_training:
                 return

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__					     = True

           for model_class in self.all_model_classes:
                 if model_class in get_values(A__	):
                       continue
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.train()
                 lowerCamelCase__					     = self._prepare_for_class(A__		,     A__		,     return_labels=A__	)
                 lowerCamelCase__					     = model(**A__	).loss
                 loss.backward()




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = model_class(A__	)
                 lowerCamelCase__					     = inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase__					     = [*signature.parameters.keys()]

                 lowerCamelCase__					     = ['pixel_values']
                 self.assertListEqual(arg_names[:1]		,     A__	)




     @slow
     def        __snake_case      (			self     :      Dict	) ->				int:
           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = PoolFormerModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)

def 	lowerCAmelCase_	(     )    ->	Dict:


      '''simple docstring'''




      lowerCamelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image






@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @slow
     def        __snake_case      (			self     :      Any	) ->				Dict:
           lowerCamelCase__					     = PoolFormerImageProcessor()
           lowerCamelCase__					     = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'	).to(A__	)

           lowerCamelCase__					     = image_processor(images=prepare_img()		,     return_tensors='pt'	).to(A__	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__					     = model(**A__	)

           # verify the logits
           lowerCamelCase__					     = torch.Size((1, 1_0_0_0)	)
           self.assertEqual(outputs.logits.shape		,     A__	)

           lowerCamelCase__					     = torch.tensor([-0.6113, 0.1685, -0.0492]	).to(A__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     A__		,     atol=1E-4	)	)