


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Optional[Any]        =						IFInpaintingPipeline
      snake_case__				:     Dict        =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
      snake_case__				:     Dict        =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      snake_case__				:     List[str]        =						PipelineTesterMixin.required_optional_params - {'''latents'''}
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             return self._get_dummy_components()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any=0     )		->			str:
             if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                    a_					:		Any							=  torch.manual_seed(SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		List[str]							=  torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  floats_tensor((1, 3, 3_2, 3_2)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     ).to(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  floats_tensor((1, 3, 3_2, 3_2)     ,							rng=random.Random(SCREAMING_SNAKE_CASE__     )     ).to(SCREAMING_SNAKE_CASE__     )

             a_					:		Dict							=  {
                 'prompt': 'A painting of a squirrel eating a burger',
                 'image': image,
                 'mask_image': mask_image,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'output_type': 'numpy',
             }

             return inputs
      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()     ,							reason='XFormers attention is only available with CUDA and `xformers` installed'     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != 'cuda'     ,							reason='float16 requires CUDA'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Union[str, Any]:
             # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
             super().test_save_load_floataa(expected_max_diff=1E-1     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             self._test_save_load_local()

      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Tuple:
             self._test_inference_batch_single_identical(
                 expected_max_diff=1E-2     ,							)






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase_      : Dict  					=       {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      snake_case__				:     List[str]        =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__				:     Optional[Any]        =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            snake_case__				:     str        =						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            snake_case__				:     List[Any]        =						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     )

             a_					:		int							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]     )

             a_					:		List[str]							=  text_classifier(['This is great !', 'This is bad']     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=1     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             # Legacy behavior
             a_					:		Union[str, Any]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		List[str]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]     )

             a_					:		int							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		str							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     {'label': 'LABEL_0', 'score': 0.504},
                     {'label': 'LABEL_0', 'score': 0.504},
                 ]     ,							)
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             import torch

             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     ,							device=torch.device('cpu'     )     ,							)

             a_					:		Any							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		List[str]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='tf'     )

             a_					:		Optional[int]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @slow
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		List[str]							=  pipeline('text-classification'     )

             a_					:		Dict							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		Union[str, Any]							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Tuple							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      @slow
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_					:		Dict							=  pipeline('text-classification'     ,							framework='tf'     )

             a_					:		Optional[Any]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		int							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Optional[int]							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Optional[Any]							=  TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__     ,							tokenizer=SCREAMING_SNAKE_CASE__     )
             return text_classifier, ["HuggingFace is in", "This is another test"]

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Union[str, Any]:
             a_					:		List[str]							=  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             a_					:		Union[str, Any]							=  'HuggingFace is in'
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )

             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     )
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )

             a_					:		Union[str, Any]							=  ['HuggingFace is in ', 'Paris is in France']
             a_					:		int							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}, {'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )
             self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()     )

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             a_					:		List[Any]							=  text_classifier(SCREAMING_SNAKE_CASE__     ,							top_k=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  len(model.config.idalabel.values()     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}] * N]     ,							)

             a_					:		int							=  {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
             a_					:		Optional[int]							=  text_classifier(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}     ,							)
             self.assertTrue(outputs['label'] in model.config.idalabel.values()     )

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             a_					:		Any							=  [['HuggingFace is in ', 'Paris is in France']]
             with self.assertRaises(SCREAMING_SNAKE_CASE__     ):
                    text_classifier(SCREAMING_SNAKE_CASE__     )

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             a_					:		Tuple							=  text_classifier([[['HuggingFace is in ', 'Paris is in France']]]     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': ANY(SCREAMING_SNAKE_CASE__     ), 'score': ANY(SCREAMING_SNAKE_CASE__     )}]     ,							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()     )


