

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 					A		(				__SCREAMING_SNAKE_CASE       ,				unittest.TestCase					):







    """simple docstring"""





    lowerCamelCase						      =				KandinskyPipeline
    lowerCamelCase						      =				[
        'prompt',
        'image_embeds',
        'negative_image_embeds',
    ]
    lowerCamelCase						      =				['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
    lowerCamelCase						      =				[
        'generator',
        'height',
        'width',
        'latents',
        'guidance_scale',
        'negative_prompt',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowerCamelCase						      =				False


    @property
    def 		snake_case__	( self					:			Union[str, Any]						)->       Tuple:





        '''simple docstring'''







        return 3_2


    @property
    def 		snake_case__	( self					:			Tuple						)->       List[str]:





        '''simple docstring'''







        return 3_2


    @property
    def 		snake_case__	( self					:			Optional[Any]						)->       Optional[int]:





        '''simple docstring'''







        return self.time_input_dim


    @property
    def 		snake_case__	( self					:			Tuple						)->       Optional[Any]:





        '''simple docstring'''







        return self.time_input_dim * 4


    @property
    def 		snake_case__	( self					:			Any						)->       List[Any]:





        '''simple docstring'''







        return 1_0_0


    @property
    def 		snake_case__	( self					:			List[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
        return tokenizer


    @property
    def 		snake_case__	( self					:			List[Any]						)->       List[Any]:





        '''simple docstring'''







        torch.manual_seed(0						)
        A__        =  MCLIPConfig(
            numDims=self.cross_attention_dim,transformerDimensions=self.text_embedder_hidden_size,hidden_size=self.text_embedder_hidden_size,intermediate_size=3_7,num_attention_heads=4,num_hidden_layers=5,vocab_size=1_0_0_5,)

        A__        =  MultilingualCLIP(__UpperCAmelCase						)
        A__        =  text_encoder.eval()

        return text_encoder


    @property
    def 		snake_case__	( self					:			str						)->       str:





        '''simple docstring'''







        torch.manual_seed(0						)

        A__        =  {
            'in_channels': 4,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'text_image',
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'encoder_hid_dim': self.text_embedder_hidden_size,
            'encoder_hid_dim_type': 'text_image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        A__        =  UNetaDConditionModel(**__UpperCAmelCase						)
        return model


    @property
    def 		snake_case__	( self					:			List[str]						)->       Tuple:





        '''simple docstring'''







        return {
            "block_out_channels": [3_2, 6_4],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 1_2,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }


    @property
    def 		snake_case__	( self					:			Dict						)->       Optional[int]:





        '''simple docstring'''







        torch.manual_seed(0						)
        A__        =  VQModel(**self.dummy_movq_kwargs						)
        return model


    def 		snake_case__	( self					:			Dict						)->       Optional[int]:





        '''simple docstring'''







        A__        =  self.dummy_text_encoder
        A__        =  self.dummy_tokenizer
        A__        =  self.dummy_unet
        A__        =  self.dummy_movq

        A__        =  DDIMScheduler(
            num_train_timesteps=1_0_0_0,beta_schedule='linear',beta_start=0.00_085,beta_end=0.012,clip_sample=__UpperCAmelCase,set_alpha_to_one=__UpperCAmelCase,steps_offset=1,prediction_type='epsilon',thresholding=__UpperCAmelCase,)

        A__        =  {
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }
        return components


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			List[str],lowercase_					:			List[str]=0						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  floats_tensor((1, self.cross_attention_dim),rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
        A__        =  floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1						)						).to(__UpperCAmelCase						)
        if str(__UpperCAmelCase						).startswith('mps'						):
            A__        =  torch.manual_seed(__UpperCAmelCase						)
        else:
            A__        =  torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
        A__        =  {
            'prompt': 'horse',
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 6_4,
            'width': 6_4,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs


    def 		snake_case__	( self					:			str						)->       int:





        '''simple docstring'''







        A__        =  'cpu'

        A__        =  self.get_dummy_components()

        A__        =  self.pipeline_class(**__UpperCAmelCase						)
        A__        =  pipe.to(__UpperCAmelCase						)

        pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

        A__        =  pipe(**self.get_dummy_inputs(__UpperCAmelCase						)						)
        A__        =  output.images

        A__        =  pipe(
            **self.get_dummy_inputs(__UpperCAmelCase						),return_dict=__UpperCAmelCase,)[0]

        A__        =  image[0, -3:, -3:, -1]
        A__        =  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        A__        =  np.array(
            [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]						)

        assert (
            np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
        ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
        ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'




@slow
@require_torch_gpu
class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			Any						)->       Any:





        '''simple docstring'''







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


    def 		snake_case__	( self					:			int						)->       Any:





        '''simple docstring'''







        A__        =  load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_text2img_cat_fp16.npy'						)

        A__        =  KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior',torch_dtype=torch.floataa						)
        pipe_prior.to(__UpperCAmelCase						)

        A__        =  KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1',torch_dtype=torch.floataa						)
        A__        =  pipeline.to(__UpperCAmelCase						)
        pipeline.set_progress_bar_config(disable=__UpperCAmelCase						)

        A__        =  'red cat, 4k photo'

        A__        =  torch.Generator(device='cuda'						).manual_seed(0						)
        A__ ,		A__        =  pipe_prior(
            __UpperCAmelCase,generator=__UpperCAmelCase,num_inference_steps=5,negative_prompt='',).to_tuple()

        A__        =  torch.Generator(device='cuda'						).manual_seed(0						)
        A__        =  pipeline(
            __UpperCAmelCase,image_embeds=__UpperCAmelCase,negative_image_embeds=__UpperCAmelCase,generator=__UpperCAmelCase,num_inference_steps=1_0_0,output_type='np',)

        A__        =  output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        assert_mean_pixel_difference(__UpperCAmelCase,__UpperCAmelCase						)



"""simple docstring"""

import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase      :							List[str]          =					"\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

UpperCamelCase      :							int          =					"\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

UpperCamelCase      :							Optional[Any]          =					"\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - 'id': id of the question-answer pair (see above),\n            - 'answers': a list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,    _KWARGS_DESCRIPTION     )
class __lowerCAmelCase						( datasets.Metric     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return datasets.MetricInfo(
          description=_DESCRIPTION      ,	citation=_CITATION      ,	inputs_description=_KWARGS_DESCRIPTION      ,	features=datasets.Features(
              {
                  'predictions': {
                      'id': datasets.Value('string' ),
                      'prediction_text': datasets.Value('string' ),
                      'no_answer_probability': datasets.Value('float32' ),
                  },
                  'references': {
                      'id': datasets.Value('string' ),
                      'answers': datasets.features.Sequence(
                          {'text': datasets.Value('string' ), 'answer_start': datasets.Value('int32' )} ),
                  },
              } )      ,	codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']      ,	reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']      ,	)







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=1.0 ):

      '''simple docstring'''




      __UpperCamelCase							    =			{p['id']: p['no_answer_probability'] for p in predictions}
      __UpperCamelCase							    =			[{'paragraphs': [{'qas': references}]}]
      __UpperCamelCase							    =			{p['id']: p['prediction_text'] for p in predictions}

      __UpperCamelCase							    =			make_qid_to_has_ans(__UpperCAmelCase )  # maps qid to True/False
      __UpperCamelCase							    =			[k for k, v in qid_to_has_ans.items() if v]
      __UpperCamelCase							    =			[k for k, v in qid_to_has_ans.items() if not v]

      __UpperCamelCase    ,      __UpperCamelCase							    =			get_raw_scores(__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			apply_no_ans_threshold(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			apply_no_ans_threshold(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase )

      if has_ans_qids:
       __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase      ,	qid_list=__UpperCAmelCase )
       merge_eval(__UpperCAmelCase      ,	__UpperCAmelCase      ,	'HasAns' )
      if no_ans_qids:
       __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase      ,	qid_list=__UpperCAmelCase )
       merge_eval(__UpperCAmelCase      ,	__UpperCAmelCase      ,	'NoAns' )
      find_all_best_thresh(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      return dict(__UpperCAmelCase )



