"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					UpperCAmelCase_     (   _a			,							unittest.TestCase):
						lowerCamelCase__		:   Tuple			  =  KandinskyPriorPipeline
						lowerCamelCase__		:   Tuple			  =  ["prompt"]
						lowerCamelCase__		:   List[str]			  =  ["prompt", "negative_prompt"]
						lowerCamelCase__		:   List[Any]			  =  [
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						lowerCamelCase__		:   Optional[Any]			  =  False


						@property
						def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
													return 3_2


						@property
						def 		_UpperCAmelCase	(       self							)	-> str:
													return 3_2


						@property
						def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
													return self.time_input_dim


						@property
						def 		_UpperCAmelCase	(       self							)	-> Any:
													return self.time_input_dim * 4


						@property
						def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
													return 1_0_0


						@property
						def 		_UpperCAmelCase	(       self							)	-> List[Any]:
													lowercase__      :		Tuple          =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
													return tokenizer


						@property
						def 		_UpperCAmelCase	(       self							)	-> Any:
													torch.manual_seed(0							)
													lowercase__      :		Optional[int]          =     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(a							)


						@property
						def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
													torch.manual_seed(0							)

													lowercase__      :		int          =     {
													    'num_attention_heads': 2,
													    'attention_head_dim': 1_2,
													    'embedding_dim': self.text_embedder_hidden_size,
													    'num_layers': 1,
													}

													lowercase__      :		Tuple          =     PriorTransformer(**a							)
													# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
													lowercase__      :		int          =     nn.Parameter(torch.ones(model.clip_std.shape							)							)
													return model


						@property
						def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
													torch.manual_seed(0							)
													lowercase__      :		Optional[int]          =     CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size		,      image_size=2_2_4		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1_4		,      )

													lowercase__      :		Optional[Any]          =     CLIPVisionModelWithProjection(a							)
													return model


						@property
						def 		_UpperCAmelCase	(       self							)	-> Tuple:
													lowercase__      :		Dict          =     CLIPImageProcessor(
													    crop_size=2_2_4		,      do_center_crop=a		,      do_normalize=a		,      do_resize=a		,      image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,      image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,      resample=3		,      size=2_2_4		,      )

													return image_processor


						def 		_UpperCAmelCase	(       self							)	-> Dict:
													lowercase__      :		List[str]          =     self.dummy_prior
													lowercase__      :		Tuple          =     self.dummy_image_encoder
													lowercase__      :		Optional[Any]          =     self.dummy_text_encoder
													lowercase__      :		List[str]          =     self.dummy_tokenizer
													lowercase__      :		Optional[int]          =     self.dummy_image_processor

													lowercase__      :		List[Any]          =     UnCLIPScheduler(
													    variance_type='fixed_small_log'		,      prediction_type='sample'		,      num_train_timesteps=1_0_0_0		,      clip_sample=a		,      clip_sample_range=10.0		,      )

													lowercase__      :		List[str]          =     {
													    'prior': prior,
													    'image_encoder': image_encoder,
													    'text_encoder': text_encoder,
													    'tokenizer': tokenizer,
													    'scheduler': scheduler,
													    'image_processor': image_processor,
													}

													return components


						def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Union[str, Any]:
													if str(a							).startswith('mps'							):
																				lowercase__      :		Optional[Any]          =     torch.manual_seed(a							)
													else:
																				lowercase__      :		Any          =     torch.Generator(device=a							).manual_seed(a							)
													lowercase__      :		str          =     {
													    'prompt': 'horse',
													    'generator': generator,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs


						def 		_UpperCAmelCase	(       self							)	-> Dict:
													lowercase__      :		Optional[int]          =     'cpu'

													lowercase__      :		Optional[Any]          =     self.get_dummy_components()

													lowercase__      :		Optional[Any]          =     self.pipeline_class(**a							)
													lowercase__      :		Optional[int]          =     pipe.to(a							)

													pipe.set_progress_bar_config(disable=a							)

													lowercase__      :		Optional[int]          =     pipe(**self.get_dummy_inputs(a							)							)
													lowercase__      :		Optional[Any]          =     output.image_embeds

													lowercase__      :		Dict          =     pipe(
													    **self.get_dummy_inputs(a							)		,      return_dict=a		,      )[0]

													lowercase__      :		List[str]          =     image[0, -1_0:]
													lowercase__      :		Optional[Any]          =     image_from_tuple[0, -1_0:]

													assert image.shape == (1, 3_2)

													lowercase__      :		Union[str, Any]          =     np.array(
													    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

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


						@skip_mps
						def 		_UpperCAmelCase	(       self							)	-> List[Any]:
													lowercase__      :		Tuple          =     torch_device == 'cpu'
													lowercase__      :		Union[str, Any]          =     True
													lowercase__      :		int          =     False

													self._test_inference_batch_single_identical(
													    test_max_difference=a		,      relax_max_difference=a		,      test_mean_pixel_difference=a		,      )


						@skip_mps
						def 		_UpperCAmelCase	(       self							)	-> int:
													lowercase__      :		Tuple          =     torch_device == 'cpu'
													lowercase__      :		Any          =     False

													self._test_attention_slicing_forward_pass(
													    test_max_difference=a		,      test_mean_pixel_difference=a		,      )


"""simple docstring"""





import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
       import importlib.resources as pkg_resources
except ImportError:
       # Try backported to PY<37 `importlib_resources`.
       import importlib_resources as pkg_resources

from . import resources


_UpperCamelCase       :						Optional[int]     =   "https://github.com/huggingface/datasets/tree/main/src/datasets/utils"
_UpperCamelCase       :						Union[str, Any]     =   f'''{BASE_REF_URL}/{__file__}'''
_UpperCamelCase       :						Any     =   logging.getLogger(__name__)





def     a_					(				_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     pkg_resources.read_text(_lowerCAmelCase       ,			_lowerCAmelCase					)
       return yaml.safe_load(_lowerCAmelCase					), f"""{BASE_REF_URL}/resources/{resource}"""


_UpperCamelCase   ,				_UpperCamelCase       :						int     =   load_yaml_resource("readme_structure.yaml")

_UpperCamelCase       :						Optional[Any]     =   [
    "[Needs More Information]",
    "[More Information Needed]",
    "(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)",
]

# Dictionary representation of section/readme, error_list, warning_list
_UpperCamelCase       :						List[Any]     =   Tuple[dict, List[str], List[str]]


class 					UpperCAmelCase_     :


      def __init__(       self		,      a		,      a		,      a = None		,      a = False							)	-> str:
             lowercase__      :		List[str]          =     name
             lowercase__      :		int          =     level
             lowercase__      :		Dict          =     lines
             lowercase__      :		Optional[int]          =     ''
             lowercase__      :		List[str]          =     True
             lowercase__      :		Union[str, Any]          =     {}
             lowercase__      :		Any          =     []
             lowercase__      :		Optional[int]          =     []
             if self.lines is not None:
                    self.parse(suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self		,      a = False							)	-> Any:
             lowercase__      :		List[str]          =     ''
             lowercase__      :		int          =     []
             lowercase__      :		Any          =     False
             for line in self.lines:
                    if line.strip(' \n'							) == "":
                           continue
                    elif line.strip(' \n'							)[:3] == "```":
                           lowercase__      :		Optional[Any]          =     not code_start
                    elif line.split()[0] == self.level + "#" and not code_start:
                           if current_sub_level != "":
                                  lowercase__      :		Tuple          =     Section(a		,      self.level + '#'		,      a							)
                                  lowercase__      :		str          =     []
                           else:
                                  if current_lines != []:
                                         self.text += "".join(a							).strip()
                                         if self.text != "" and self.text not in FILLER_TEXT:
                                                lowercase__      :		List[Any]          =     False
                                         lowercase__      :		List[str]          =     []

                           lowercase__      :		Dict          =     ' '.join(line.split()[1:]							).strip(' \n'							)
                    else:
                           current_lines.append(a							)
             else:
                    if current_sub_level != "":
                           if current_sub_level in self.content:
                                  self.parsing_error_list.append(
                                      f"""Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections."""							)
                           lowercase__      :		Tuple          =     Section(a		,      self.level + '#'		,      a							)
                    else:
                           if current_lines != []:
                                  self.text += "".join(a							).strip()
                                  if self.text != "" and self.text not in FILLER_TEXT:
                                         lowercase__      :		Dict          =     False

             if self.level == "" and not suppress_parsing_errors:
                    if self.parsing_error_list != [] or self.parsing_warning_list != []:
                           lowercase__      :		Optional[Any]          =     '\n'.join('-\t' + x for x in self.parsing_error_list + self.parsing_warning_list							)
                           lowercase__      :		int          =     f"""The following issues were found while parsing the README at `{self.name}`:\n""" + errors
                           raise ValueError(a							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> ReadmeValidatorOutput:
             lowercase__      :		Tuple          =     []
             lowercase__      :		int          =     []
             if structure["allow_empty"] is False:
                    # If content is expected
                    if self.is_empty_text and self.content == {}:
                           # If no content is found, mention it in the error_list
                           error_list.append(f"""Expected some content in section `{self.name}` but it is empty."""							)

             if structure["allow_empty_text"] is False:
                    # If some text is expected
                    if self.is_empty_text:
                           # If no text is found, mention it in the error_list
                           error_list.append(
                               f"""Expected some text in section `{self.name}` but it is empty (text in subsections are ignored)."""							)
        # Subsections Validation
             if structure["subsections"] is not None:
                    # If subsections are expected
                    if self.content == {}:
                           # If no subsections are present
                           lowercase__      :		str          =     [subsection['name'] for subsection in structure['subsections']]
                           # Mention the expected values in the error_list
                           error_list.append(
                               f"""Section `{self.name}` expected the following subsections: {", ".join(["`"+x+"`" for x in values]							)}. Found 'None'."""							)
                    else:
                           # If some subsections are present
                           lowercase__      :		Union[str, Any]          =     [subsection['name'] for subsection in structure['subsections']]
                           lowercase__      :		Any          =     False
                           for idx, name in enumerate(a							):
                                  if name not in self.content:
                                         # If the expected subsection is not present
                                         error_list.append(f"""Section `{self.name}` is missing subsection: `{name}`."""							)
                                         lowercase__      :		str          =     True
                                  else:
                                         # If the subsection is present, validate subsection, return the result
                                         # and concat the errors from subsection to section error_list

                                         # Skip sublevel validation if current level is `###`
                                         if self.level == "###":
                                                continue
                                         else:
                                                lowercase__   ,				lowercase__   ,				lowercase__      :		Optional[Any]          =     self.content[name].validate(
                                                    structure['subsections'][idx]							)
                                         error_list += subsec_error_list
                                         warning_list += subsec_warning_list

                           if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
                                  for name in self.content:
                                         if name not in structure_names:
                                                # If an extra subsection is present
                                                warning_list.append(
                                                    f"""`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown."""							)
             if error_list:
                    # If there are errors, do not return the dictionary as it is invalid
                    return {}, error_list, warning_list
             else:
                    return self.to_dict(), error_list, warning_list


      def 		_UpperCAmelCase	(       self							)	-> dict:
             return {
                 "name": self.name,
                 "text": self.text,
                 "is_empty_text": self.is_empty_text,
                 "subsections": [value.to_dict() for value in self.content.values()],
             }





class 					UpperCAmelCase_     (   _a):  # Level 0


      def __init__(       self		,      a		,      a		,      a = None		,      a = False							)	-> Any:
             super().__init__(name=a		,      level=''							)  # Not using lines here as we need to use a child class parse
             lowercase__      :		List[Any]          =     structure
             lowercase__      :		Dict          =     -2
             lowercase__      :		Union[str, Any]          =     0
             lowercase__      :		Dict          =     lines
             if self.lines is not None:
                    self.parse(suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             if self.structure is None:
                    lowercase__   ,				lowercase__   ,				lowercase__      :		Any          =     self._validate(a							)
             else:
                    lowercase__   ,				lowercase__   ,				lowercase__      :		Dict          =     self._validate(self.structure							)
             if error_list != [] or warning_list != []:
                    lowercase__      :		Optional[Any]          =     '\n'.join(['-\t' + x for x in error_list + warning_list]							)
                    lowercase__      :		Union[str, Any]          =     f"""The following issues were found for the README at `{self.name}`:\n""" + errors
                    raise ValueError(a							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      a		,      a = None		,      a = False							)	-> Tuple:
             with open(a		,      encoding='utf-8'							) as f:
                    lowercase__      :		List[Any]          =     f.readlines()
             return cls(a		,      a		,      a		,      suppress_parsing_errors=a							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      a		,      a = None		,      a = "root"		,      a = False							)	-> int:
             lowercase__      :		Optional[Any]          =     string.split('\n'							)
             return cls(a		,      a		,      a		,      suppress_parsing_errors=a							)


      def 		_UpperCAmelCase	(       self		,      a = False							)	-> List[Any]:
             # Skip Tags
             lowercase__      :		Tuple          =     0

             for line in self.lines:
                    self.yaml_tags_line_count += 1
                    if line.strip(' \n'							) == "---":
                           self.tag_count += 1
                           if self.tag_count == 2:
                                  break
                    line_count += 1
             if self.tag_count == 2:
                    lowercase__      :		Optional[int]          =     self.lines[line_count + 1 :]  # Get the last + 1 th item.
             else:
                    lowercase__      :		List[str]          =     self.lines[self.tag_count :]
             super().parse(suppress_parsing_errors=a							)


      def __str__(       self							)	-> Any:
             return str(self.to_dict()							)


      def 		_UpperCAmelCase	(       self		,      a							)	-> str:
             lowercase__      :		Any          =     []
             lowercase__      :		Optional[int]          =     []
             if self.yaml_tags_line_count == 0:
                    warning_list.append('Empty YAML markers are present in the README.'							)
             elif self.tag_count == 0:
                    warning_list.append('No YAML markers are present in the README.'							)
             elif self.tag_count == 1:
                    warning_list.append('Only the start of YAML tags present in the README.'							)
             # Check how many first level sections are present.
             lowercase__      :		int          =     len(self.content.keys()							)
             if num_first_level_keys > 1:
                    # If more than one, add to the error list, continue
                    error_list.append(
                        f"""The README has several first-level headings: {", ".join(["`"+x+"`" for x in list(self.content.keys()							)]							)}. Only one heading is expected. Skipping further validation for this README."""							)
             elif num_first_level_keys < 1:
                    # If less than one, append error.
                    error_list.append(
                        'The README has no first-level headings. One heading is expected. Skipping further validation for this README.'							)

             else:
                    # If one exactly
                    lowercase__      :		Dict          =     list(self.content.keys()							)[0]  # Get the key
                    if start_key.startswith('Dataset Card for'							):  # Check correct start
                           # If the starting is correct, validate all the sections
                           lowercase__   ,				lowercase__   ,				lowercase__      :		Any          =     self.content[start_key].validate(
                               readme_structure['subsections'][0]							)
                           error_list += sec_error_list
                           warning_list += sec_warning_list
                    else:
                           # If not found, append error
                           error_list.append(
                               'No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'							)
             if error_list:
                    # If there are errors, do not return the dictionary as it is invalid
                    return {}, error_list, warning_list
             else:
                    return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
       from argparse import ArgumentParser

       _UpperCamelCase       :						Tuple     =   ArgumentParser(usage="Validate the content (excluding YAML tags) of a README.md file.")
       ap.add_argument("readme_filepath")
       _UpperCamelCase       :						str     =   ap.parse_args()
       _UpperCamelCase       :						Optional[int]     =   Path(args.readme_filepath)
       _UpperCamelCase       :						Dict     =   ReadMe.from_readme(readme_filepath)

