





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class   A__ (   _a	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		MvpTokenizer
    __magic_name__        =		MvpTokenizerFast
    __magic_name__        =		True
    __magic_name__        =		filter_roberta_detectors
    def 		a_      (	self   ):
         super().setUp()
         snake_case					=    [
             """l""",
             """o""",
             """w""",
             """e""",
             """r""",
             """s""",
             """t""",
             """i""",
             """d""",
             """n""",
             """\u0120""",
             """\u0120l""",
             """\u0120n""",
             """\u0120lo""",
             """\u0120low""",
             """er""",
             """\u0120lowest""",
             """\u0120newer""",
             """\u0120wider""",
             """<unk>""",
         ]
         snake_case					=    dict(zip(snake_case_      , range(len(snake_case_   )   )   )   )
         snake_case					=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         snake_case					=    {"""unk_token""": """<unk>"""}

         snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''vocab_file''']   )
         snake_case					=    os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES['''merges_file''']   )
         with open(self.vocab_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
              fp.write(json.dumps(snake_case_   ) + '''\n'''   )
         with open(self.merges_file      , '''w'''      , encoding='''utf-8'''   ) as fp:
              fp.write('''\n'''.join(snake_case_   )   )
    def 		a_      (	self      , **__snake_case   ):
         kwargs.update(self.special_tokens_map   )
         return self.tokenizer_class.from_pretrained(self.tmpdirname      , **snake_case_   )
    def 		a_      (	self      , **__snake_case   ):
         kwargs.update(self.special_tokens_map   )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      , **snake_case_   )
    def 		a_      (	self      , __snake_case   ):
         return "lower newer", "lower newer"
    @cached_property
    def 		a_      (	self   ):
         return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''   )
    @cached_property
    def 		a_      (	self   ):
         return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         snake_case					=    [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(snake_case_      , max_length=len(snake_case_   )      , padding=snake_case_      , return_tensors='''pt'''   )
              self.assertIsInstance(snake_case_      , snake_case_   )

              self.assertEqual((2, 9)      , batch.input_ids.shape   )
              self.assertEqual((2, 9)      , batch.attention_mask.shape   )
              snake_case					=    batch.input_ids.tolist()[0]
              self.assertListEqual(snake_case_      , snake_case_   )
              # Test that special tokens are reset
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(snake_case_      , padding=snake_case_      , return_tensors='''pt'''   )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''      , snake_case_   )
              self.assertIn('''attention_mask'''      , snake_case_   )
              self.assertNotIn('''labels'''      , snake_case_   )
              self.assertNotIn('''decoder_attention_mask'''      , snake_case_   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    [
             """Summary of the text.""",
             """Another summary.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(text_target=snake_case_      , max_length=3_2      , padding='''max_length'''      , return_tensors='''pt'''   )
              self.assertEqual(3_2      , targets['''input_ids'''].shape[1]   )
    @require_torch
    def 		a_      (	self   ):
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(
                  ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']      , padding=snake_case_      , truncation=snake_case_      , return_tensors='''pt'''   )
              self.assertIsInstance(snake_case_      , snake_case_   )
              self.assertEqual(batch.input_ids.shape      , (2, 1_0_2_4)   )
    @require_torch
    def 		a_      (	self   ):
         snake_case					=    ["""A long paragraph for summarization."""]
         snake_case					=    [
             """Summary of the text.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=    tokenizer(snake_case_      , text_target=snake_case_      , return_tensors='''pt'''   )
              snake_case					=    inputs["""input_ids"""]
              snake_case					=    inputs["""labels"""]
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )
    def 		a_      (	self   ):
         pass




    def 		a_      (	self   ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
                   snake_case					=    self.rust_tokenizer_class.from_pretrained(snake_case_      , **snake_case_   )
                   snake_case					=    self.tokenizer_class.from_pretrained(snake_case_      , **snake_case_   )
                   snake_case					=    """A, <mask> AllenNLP sentence."""
                   snake_case					=    tokenizer_r.encode_plus(snake_case_      , add_special_tokens=snake_case_      , return_token_type_ids=snake_case_   )
                   snake_case					=    tokenizer_p.encode_plus(snake_case_      , add_special_tokens=snake_case_      , return_token_type_ids=snake_case_   )

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['''token_type_ids''']   )      , sum(tokens_p['''token_type_ids''']   )   )

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']   ) / len(tokens_r['''attention_mask''']   )      , sum(tokens_p['''attention_mask''']   ) / len(tokens_p['''attention_mask''']   )      , )

                   snake_case					=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
                   snake_case					=    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']   )

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['''input_ids''']      , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )
                   self.assertSequenceEqual(tokens_r['''input_ids''']      , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )

                   self.assertSequenceEqual(
                       snake_case_      , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )
                   self.assertSequenceEqual(
                       snake_case_      , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']   )








import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class   A__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
    def 		a_      (	self      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 3, 1_2_8, 1_2_8)      , rng=random.Random(__snake_case   )   )
         snake_case					=    np.random.RandomState(__snake_case   )
         snake_case					=    {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.75,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]   )
         assert np.abs(image_slice - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    PNDMScheduler.from_config(pipe.scheduler.config      , skip_prk_steps=__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         # warmup pass to apply optimizations
         snake_case					=    pipe(**self.get_dummy_inputs()   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1
    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1




    def 		a_      (	self   ):
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      , provider='''CPUExecutionProvider'''   )
         snake_case					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs()
         snake_case					=    pipe(**__snake_case   ).images
         snake_case					=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case					=    np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-1






@nightly
@require_onnxruntime
@require_torch_gpu
class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    @property
    def 		a_      (	self   ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def 		a_      (	self   ):
         snake_case					=    ort.SessionOptions()
         snake_case					=    False
         return options
    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         # using the PNDM scheduler by default
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''      , revision='''onnx'''      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=1_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2




    def 		a_      (	self   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''   )
         snake_case					=    init_image.resize((7_6_8, 5_1_2)   )
         snake_case					=    LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , subfolder='''scheduler'''      , revision='''onnx'''   )
         snake_case					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''      , revision='''onnx'''      , scheduler=__snake_case      , safety_checker=__snake_case      , feature_extractor=__snake_case      , provider=self.gpu_provider      , sess_options=self.gpu_options      , )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''A fantasy landscape, trending on artstation'''

         snake_case					=    np.random.RandomState(0   )
         snake_case					=    pipe(
             prompt=__snake_case      , image=__snake_case      , strength=0.75      , guidance_scale=7.5      , num_inference_steps=2_0      , generator=__snake_case      , output_type='''np'''      , )
         snake_case					=    output.images
         snake_case					=    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case					=    np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]   )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2E-2




