




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 				__a (			__lowerCamelCase							,					unittest.TestCase					):


					"""simple docstring"""


					_A    :   int     =  UnCLIPPipeline
					_A    :   int     =  TEXT_TO_IMAGE_PARAMS - {
					    "negative_prompt",
					    "height",
					    "width",
					    "negative_prompt_embeds",
					    "guidance_scale",
					    "prompt_embeds",
					    "cross_attention_kwargs",
					}
					_A    :   Union[str, Any]     =  TEXT_TO_IMAGE_BATCH_PARAMS
					_A    :   Optional[int]     =  [
					    "generator",
					    "return_dict",
					    "prior_num_inference_steps",
					    "decoder_num_inference_steps",
					    "super_res_num_inference_steps",
					]
					_A    :   List[str]     =  False




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


									'''simple docstring'''





									return 3_2




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


									'''simple docstring'''





									return 3_2




					@property
					def   __A    (     self      :			Union[str, Any]						)		->       List[Any]:


									'''simple docstring'''





									return self.time_input_dim




					@property
					def   __A    (     self      :			Optional[Any]						)		->       Union[str, Any]:


									'''simple docstring'''





									return self.time_input_dim * 4




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


									'''simple docstring'''





									return 1_0_0




					@property
					def   __A    (     self      :			int						)		->       int:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
									return tokenizer




					@property
					def   __A    (     self      :			Union[str, Any]						)		->       Optional[Any]:


									'''simple docstring'''





									torch.manual_seed(0						)
									SCREAMING_SNAKE_CASE__       =CLIPTextConfig(
									    bos_token_id=0    ,eos_token_id=2    ,hidden_size=self.text_embedder_hidden_size    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=3_7    ,layer_norm_eps=1e-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_0_0_0    ,)
									return CLIPTextModelWithProjection(_UpperCamelCase						)




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


									'''simple docstring'''





									torch.manual_seed(0						)

									SCREAMING_SNAKE_CASE__       ={
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 1_2,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									SCREAMING_SNAKE_CASE__       =PriorTransformer(**_UpperCamelCase						)
									return model




					@property
					def   __A    (     self      :			List[str]						)		->       Optional[int]:


									'''simple docstring'''





									torch.manual_seed(0						)

									SCREAMING_SNAKE_CASE__       ={
									    """clip_embeddings_dim""": self.text_embedder_hidden_size,
									    """time_embed_dim""": self.time_embed_dim,
									    """cross_attention_dim""": self.cross_attention_dim,
									}

									SCREAMING_SNAKE_CASE__       =UnCLIPTextProjModel(**_UpperCamelCase						)
									return model




					@property
					def   __A    (     self      :			List[Any]						)		->       Union[str, Any]:


									'''simple docstring'''





									torch.manual_seed(0						)

									SCREAMING_SNAKE_CASE__       ={
									    """sample_size""": 3_2,
									    # RGB in channels
									    """in_channels""": 3,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 6,
									    """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,
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": """identity""",
									}

									SCREAMING_SNAKE_CASE__       =UNetaDConditionModel(**_UpperCamelCase						)
									return model




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


									'''simple docstring'''





									return {
									    "sample_size": 6_4,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}




					@property
					def   __A    (     self      :			int						)		->       Tuple:


									'''simple docstring'''





									torch.manual_seed(0						)

									SCREAMING_SNAKE_CASE__       =UNetaDModel(**self.dummy_super_res_kwargs						)
									return model




					@property
					def   __A    (     self      :			Union[str, Any]						)		->       int:


									'''simple docstring'''





									torch.manual_seed(1						)

									SCREAMING_SNAKE_CASE__       =UNetaDModel(**self.dummy_super_res_kwargs						)
									return model




					def   __A    (     self      :			Optional[int]						)		->       str:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.dummy_prior
									SCREAMING_SNAKE_CASE__       =self.dummy_decoder
									SCREAMING_SNAKE_CASE__       =self.dummy_text_proj
									SCREAMING_SNAKE_CASE__       =self.dummy_text_encoder
									SCREAMING_SNAKE_CASE__       =self.dummy_tokenizer
									SCREAMING_SNAKE_CASE__       =self.dummy_super_res_first
									SCREAMING_SNAKE_CASE__       =self.dummy_super_res_last

									SCREAMING_SNAKE_CASE__       =UnCLIPScheduler(
									    variance_type="""fixed_small_log"""    ,prediction_type="""sample"""    ,num_train_timesteps=1_0_0_0    ,clip_sample_range=5.0    ,)

									SCREAMING_SNAKE_CASE__       =UnCLIPScheduler(
									    variance_type="""learned_range"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_0_0_0    ,)

									SCREAMING_SNAKE_CASE__       =UnCLIPScheduler(
									    variance_type="""fixed_small_log"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_0_0_0    ,)

									SCREAMING_SNAKE_CASE__       ={
									    """prior""": prior,
									    """decoder""": decoder,
									    """text_proj""": text_proj,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """super_res_first""": super_res_first,
									    """super_res_last""": super_res_last,
									    """prior_scheduler""": prior_scheduler,
									    """decoder_scheduler""": decoder_scheduler,
									    """super_res_scheduler""": super_res_scheduler,
									}

									return components




					def   __A    (     self      :			str    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			int=0						)		->       Optional[int]:


									'''simple docstring'''





									if str(_UpperCamelCase						).startswith("""mps"""						):
													SCREAMING_SNAKE_CASE__       =torch.manual_seed(_UpperCamelCase						)
									else:
													SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
									SCREAMING_SNAKE_CASE__       ={
									    """prompt""": """horse""",
									    """generator""": generator,
									    """prior_num_inference_steps""": 2,
									    """decoder_num_inference_steps""": 2,
									    """super_res_num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}
									return inputs




					def   __A    (     self      :			List[Any]						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       ="""cpu"""

									SCREAMING_SNAKE_CASE__       =self.get_dummy_components()

									SCREAMING_SNAKE_CASE__       =self.pipeline_class(**_UpperCamelCase						)
									SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)

									pipe.set_progress_bar_config(disable=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       =pipe(**self.get_dummy_inputs(_UpperCamelCase						)						)
									SCREAMING_SNAKE_CASE__       =output.images

									SCREAMING_SNAKE_CASE__       =pipe(
									    **self.get_dummy_inputs(_UpperCamelCase						)    ,return_dict=_UpperCamelCase    ,)[0]

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

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

									SCREAMING_SNAKE_CASE__       =np.array(
									    [
									        0.9997,
									        0.9988,
									        0.0028,
									        0.9997,
									        0.9984,
									        0.9965,
									        0.0029,
									        0.9986,
									        0.0025,
									    ]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2




					def   __A    (     self      :			Any						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =torch.device("""cpu"""						)
									class 				__a :


														"""simple docstring"""


														_A    :   int     =  1






									SCREAMING_SNAKE_CASE__       =self.get_dummy_components()

									SCREAMING_SNAKE_CASE__       =self.pipeline_class(**_UpperCamelCase						)
									SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       =components["""prior"""]
									SCREAMING_SNAKE_CASE__       =components["""decoder"""]
									SCREAMING_SNAKE_CASE__       =components["""super_res_first"""]
									SCREAMING_SNAKE_CASE__       =components["""tokenizer"""]
									SCREAMING_SNAKE_CASE__       =components["""text_encoder"""]

									SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(0						)
									SCREAMING_SNAKE_CASE__       =prior.dtype
									SCREAMING_SNAKE_CASE__       =1

									SCREAMING_SNAKE_CASE__       =(batch_size, prior.config.embedding_dim)
									SCREAMING_SNAKE_CASE__       =pipe.prepare_latents(
									    _UpperCamelCase    ,dtype=_UpperCamelCase    ,device=_UpperCamelCase    ,generator=_UpperCamelCase    ,latents=_UpperCamelCase    ,scheduler=DummyScheduler()						)
									SCREAMING_SNAKE_CASE__       =(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
									SCREAMING_SNAKE_CASE__       =pipe.prepare_latents(
									    _UpperCamelCase    ,dtype=_UpperCamelCase    ,device=_UpperCamelCase    ,generator=_UpperCamelCase    ,latents=_UpperCamelCase    ,scheduler=DummyScheduler()						)

									SCREAMING_SNAKE_CASE__       =(
									    batch_size,
									    super_res_first.config.in_channels // 2,
									    super_res_first.config.sample_size,
									    super_res_first.config.sample_size,
									)
									SCREAMING_SNAKE_CASE__       =pipe.prepare_latents(
									    _UpperCamelCase    ,dtype=_UpperCamelCase    ,device=_UpperCamelCase    ,generator=_UpperCamelCase    ,latents=_UpperCamelCase    ,scheduler=DummyScheduler()						)

									pipe.set_progress_bar_config(disable=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       ="""this is a prompt example"""

									SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(0						)
									SCREAMING_SNAKE_CASE__       =pipe(
									    [prompt]    ,generator=_UpperCamelCase    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=_UpperCamelCase    ,decoder_latents=_UpperCamelCase    ,super_res_latents=_UpperCamelCase    ,output_type="""np"""    ,)
									SCREAMING_SNAKE_CASE__       =output.images

									SCREAMING_SNAKE_CASE__       =tokenizer(
									    _UpperCamelCase    ,padding="""max_length"""    ,max_length=tokenizer.model_max_length    ,return_tensors="""pt"""    ,)
									SCREAMING_SNAKE_CASE__       =text_encoder(text_inputs.input_ids						)
									SCREAMING_SNAKE_CASE__       =text_inputs.attention_mask

									SCREAMING_SNAKE_CASE__       =torch.Generator(device=_UpperCamelCase						).manual_seed(0						)
									SCREAMING_SNAKE_CASE__       =pipe(
									    generator=_UpperCamelCase    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=_UpperCamelCase    ,decoder_latents=_UpperCamelCase    ,super_res_latents=_UpperCamelCase    ,text_model_output=_UpperCamelCase    ,text_attention_mask=_UpperCamelCase    ,output_type="""np"""    ,)[0]

									# make sure passing text embeddings manually is identical
									assert np.abs(image - image_from_text						).max() < 1e-4




					@skip_mps
					def   __A    (     self      :			List[Any]						)		->       Dict:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase    ,expected_max_diff=0.01						)




					@skip_mps
					def   __A    (     self      :			Any						)		->       Union[str, Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =torch_device == """cpu"""
									SCREAMING_SNAKE_CASE__       =True
									SCREAMING_SNAKE_CASE__       =[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=_UpperCamelCase    ,relax_max_difference=_UpperCamelCase    ,additional_params_copy_to_batched_inputs=_UpperCamelCase    ,)




					def   __A    (     self      :			Union[str, Any]						)		->       Any:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =[
									    """prior_num_inference_steps""",
									    """decoder_num_inference_steps""",
									    """super_res_num_inference_steps""",
									]

									if torch_device == "mps":
													# TODO: MPS errors with larger batch sizes
													SCREAMING_SNAKE_CASE__       =[2, 3]
													self._test_inference_batch_consistent(
													    batch_sizes=_UpperCamelCase    ,additional_params_copy_to_batched_inputs=_UpperCamelCase    ,)
									else:
													self._test_inference_batch_consistent(
													    additional_params_copy_to_batched_inputs=_UpperCamelCase						)




					@skip_mps
					def   __A    (     self      :			Union[str, Any]						)		->       Union[str, Any]:


									'''simple docstring'''





									return super().test_dict_tuple_outputs_equivalent()




					@skip_mps
					def   __A    (     self      :			int						)		->       Any:


									'''simple docstring'''





									return super().test_save_load_local()




					@skip_mps
					def   __A    (     self      :			List[Any]						)		->       int:


									'''simple docstring'''





									return super().test_save_load_optional_components()






@nightly
class 				__a (			unittest.TestCase					):


					"""simple docstring"""


					def   __A    (     self      :			Union[str, Any]						)		->       str:


									'''simple docstring'''





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




					def   __A    (     self      :			List[Any]						)		->       str:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_cpu.npy"""						)

									SCREAMING_SNAKE_CASE__       =UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""						)
									pipeline.set_progress_bar_config(disable=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       =torch.manual_seed(0						)
									SCREAMING_SNAKE_CASE__       =pipeline(
									    """horse"""    ,num_images_per_prompt=1    ,generator=_UpperCamelCase    ,output_type="""np"""    ,)

									SCREAMING_SNAKE_CASE__       =output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)
									assert np.abs(expected_image - image						).max() < 1e-1






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


					"""simple docstring"""


					def   __A    (     self      :			List[Any]						)		->       Dict:


									'''simple docstring'''





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




					def   __A    (     self      :			Optional[Any]						)		->       Union[str, Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_fp16.npy"""						)

									SCREAMING_SNAKE_CASE__       =UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa						)
									SCREAMING_SNAKE_CASE__       =pipeline.to(_UpperCamelCase						)
									pipeline.set_progress_bar_config(disable=_UpperCamelCase						)

									SCREAMING_SNAKE_CASE__       =torch.Generator(device="""cpu"""						).manual_seed(0						)
									SCREAMING_SNAKE_CASE__       =pipeline(
									    """horse"""    ,generator=_UpperCamelCase    ,output_type="""np"""    ,)

									SCREAMING_SNAKE_CASE__       =output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									assert_mean_pixel_difference(_UpperCamelCase    ,_UpperCamelCase						)




					def   __A    (     self      :			str						)		->       Optional[Any]:


									'''simple docstring'''





									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									SCREAMING_SNAKE_CASE__       =UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa						)
									SCREAMING_SNAKE_CASE__       =pipe.to(_UpperCamelCase						)
									pipe.set_progress_bar_config(disable=_UpperCamelCase						)
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									SCREAMING_SNAKE_CASE__       =pipe(
									    """horse"""    ,num_images_per_prompt=1    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,output_type="""np"""    ,)

									SCREAMING_SNAKE_CASE__       =torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 1_0**9












from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

lowerCamelCase_  						=       {
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

lowerCamelCase_  						=       datasets.load_dataset("xnli", split="test+validation")

lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0
lowerCamelCase_  						=       0



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    if spm_diff == list(reversed(__UpperCamelCase     )     ):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(__UpperCamelCase     ) == len(__UpperCamelCase     ) and fast.decode(__UpperCamelCase     ) == fast.decode(__UpperCamelCase     ):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    SCREAMING_SNAKE_CASE__       =slow.encode(slow.decode(__UpperCamelCase     )     )
    SCREAMING_SNAKE_CASE__       =fast.encode(fast.decode(__UpperCamelCase     )     )
    if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
        # Type 3 error.
        # Snehagatha ->
        #       Sne, h, aga, th, a
        #       Sne, ha, gat, ha
        # Encoding the wrong with sp does not even recover what spm gave us
        # It fits tokenizer however...
        return True
    return False



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =fast.encode_plus(__UpperCamelCase     )[0]
    SCREAMING_SNAKE_CASE__       =enc.offsets
    SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =offsets[idx], offsets[idx - 1]
    if curr is not None and line[curr[0] : curr[1]] == "\u200f":
        return True
    if prev is not None and line[prev[0] : prev[1]] == "\u200f":
        return True



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    # Encoding can be the same with same result AAA -> A + AA vs AA + A
    # We can check that we use at least exactly the same number of tokens.
    for i, (spm_id, tok_id) in enumerate(zip(__UpperCamelCase,    __UpperCamelCase     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCamelCase     ),    reversed(__UpperCamelCase     )     )     ):
        if spm_id != tok_id:
            break
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     ) - i

    SCREAMING_SNAKE_CASE__       =spm_ids[first:last]
    SCREAMING_SNAKE_CASE__       =tok_ids[first:last]

    if check_diff(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
        return True

    if check_LTR_mark(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
        return True

    if last - first > 5:
        # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
        SCREAMING_SNAKE_CASE__       =Counter(spm_ids[first:last]     )
        SCREAMING_SNAKE_CASE__       =Counter(tok_ids[first:last]     )

        SCREAMING_SNAKE_CASE__       ={spm_ for (spm_, si) in spms.items() if toks.get(spm_,    0     ) == si}
        SCREAMING_SNAKE_CASE__       =3
        for i in range(last - first - min_width     ):
            if all(spm_ids[first + i + j] in removable_tokens for j in range(__UpperCamelCase     )     ):
                SCREAMING_SNAKE_CASE__       =[
                    k
                    for k in range(last - first - min_width     )
                    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                ]
                for j in possible_matches:
                    if check_diff(spm_ids[first : first + i],    tok_ids[first : first + j],    __UpperCamelCase,    __UpperCamelCase     ) and check_details(
                        __UpperCamelCase,    spm_ids[first + i : last],    tok_ids[first + j : last],    __UpperCamelCase,    __UpperCamelCase,    ):
                        return True

    print(f"""Spm: {[fast.decode([spm_ids[i]]     ) for i in range(__UpperCamelCase,    __UpperCamelCase     )]}"""     )
    try:
        print(f"""Tok: {[fast.decode([tok_ids[i]]     ) for i in range(__UpperCamelCase,    __UpperCamelCase     )]}"""     )
    except Exception:
        pass

    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[:first]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[last:]     )
    SCREAMING_SNAKE_CASE__       =fast.decode(spm_ids[first:last]     )
    print()
    print(__UpperCamelCase     )
    return False



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    global perfect
    global imperfect
    global wrong
    global total

    SCREAMING_SNAKE_CASE__       =slow.encode(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__       =fast.encode(__UpperCamelCase     )

    SCREAMING_SNAKE_CASE__       =False
    total += 1

    if slow_ids != fast_ids:
        if check_details(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
            SCREAMING_SNAKE_CASE__       =True
            imperfect += 1
        else:
            wrong += 1
    else:
        perfect += 1

    if total % 10_000 == 0:
        print(f"""({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"""     )

    if skip_assert:
        return

    assert (
        slow_ids == fast_ids
    ), f"""line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__UpperCamelCase     )}\n{fast.tokenize(__UpperCamelCase     )}"""



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    global batch_total
    for i in range(len(__UpperCamelCase     )     ):
        # premise, all languages
        for text in dataset[i]["premise"].values():
            test_string(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )

        # hypothesis, all languages
        for text in dataset[i]["hypothesis"]["translation"]:
            test_string(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     )


if __name__ == "__main__":
    for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
        lowerCamelCase_  						=       list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            lowerCamelCase_  						=       0
            lowerCamelCase_  						=       0
            lowerCamelCase_  						=       0
            lowerCamelCase_  						=       0

            print(f"""========================== Checking {name}: {checkpoint} ==========================""")
            lowerCamelCase_  						=       slow_class.from_pretrained(checkpoint, force_download=True)
            lowerCamelCase_  						=       fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f"""Accuracy {perfect * 100 / total:.2f}""")






