






"""simple docstring"""

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase__  			=					"""scheduler_config.json"""


class 						__lowerCamelCase							(					A__     ):





		'''simple docstring'''






		a_      :							Optional[Any]  =				1
		a_      :							Any  =				2
		a_      :							Dict  =				3
		a_      :							Any  =				4
		a_      :							Any  =				5







@dataclass
class 						__lowerCamelCase							(					A__     ):





		'''simple docstring'''






		a_      :							jnp.ndarray







class 						__lowerCamelCase							:





		'''simple docstring'''






		a_      :							Dict  =				SCHEDULER_CONFIG_NAME
		a_      :							str  =				['dtype']
		a_      :							List[str]  =				[]
		a_      :							int  =				True





		@classmethod
		def   lowerCamelCase			(  cls				:      List[Any]		,     a_				:      Dict[str, Any] = None		,     a_				:      Optional[str] = None		,     a_				:      Union[str, Any]=False		,     **a_				:      Any		,     ):
				lowerCAmelCase_							,      lowerCAmelCase_  :				Dict   							=     cls.load_config(
				    pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_		,     subfolder=SCREAMING_SNAKE_CASE_		,     return_unused_kwargs=SCREAMING_SNAKE_CASE_		,     **SCREAMING_SNAKE_CASE_		,     )
				lowerCAmelCase_							,      lowerCAmelCase_  :				Any   							=     cls.from_config(SCREAMING_SNAKE_CASE_		,     return_unused_kwargs=SCREAMING_SNAKE_CASE_		,     **SCREAMING_SNAKE_CASE_      )

				if hasattr(SCREAMING_SNAKE_CASE_		,     "create_state"      ) and getattr(SCREAMING_SNAKE_CASE_		,     "has_state"		,     SCREAMING_SNAKE_CASE_      ):
						lowerCAmelCase_  :				Any   							=     scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state





		def   lowerCamelCase			(  self				:      Optional[int]		,     a_				:      Union[str, os.PathLike]		,     a_				:      bool = False		,     **a_				:      int      ):
				self.save_config(save_directory=SCREAMING_SNAKE_CASE_		,     push_to_hub=SCREAMING_SNAKE_CASE_		,     **SCREAMING_SNAKE_CASE_      )





		@property
		def   lowerCamelCase			(  self				:      Union[str, Any]      ):
				return self._get_compatibles()





		@classmethod
		def   lowerCamelCase			(  cls				:      Union[str, Any]      ):
				lowerCAmelCase_  :				Tuple   							=     list(set([cls.__name__] + cls._compatibles      )      )
				lowerCAmelCase_  :				str   							=     importlib.import_module(__name__.split("."      )[0]      )
				lowerCAmelCase_  :				Dict   							=     [
				    getattr(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_      ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE_		,     SCREAMING_SNAKE_CASE_      )
				]
				return compatible_classes







def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase			)		->       List[str]:

		"""simple docstring"""







		assert len(snake_case__			) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case__			) - x.ndim)			) ,			snake_case__			)

def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase=0.9_99 ,			__UpperCamelCase=jnp.floataa			)		->       List[Any]:

		"""simple docstring"""







		def alpha_bar(__UpperCamelCase			):
				return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

		lowerCAmelCase_  :				Any   							=     []
		for i in range(snake_case__			):
				lowerCAmelCase_  :				List[Any]   							=     i / num_diffusion_timesteps
				lowerCAmelCase_  :				Any   							=     (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(snake_case__			) / alpha_bar(snake_case__			) ,			snake_case__			)			)
		return jnp.array(snake_case__ ,			dtype=snake_case__			)


@flax.struct.dataclass
class 						__lowerCamelCase							:





		'''simple docstring'''






		a_      :							jnp.ndarray
		a_      :							jnp.ndarray
		a_      :							jnp.ndarray





		@classmethod
		def   lowerCamelCase			(  cls				:      Union[str, Any]		,     a_				:      Union[str, Any]      ):
				lowerCAmelCase_  :				str   							=     scheduler.config

				if config.trained_betas is not None:
						lowerCAmelCase_  :				Optional[int]   							=     jnp.asarray(config.trained_betas		,     dtype=scheduler.dtype      )
				elif config.beta_schedule == "linear":
						lowerCAmelCase_  :				int   							=     jnp.linspace(config.beta_start		,     config.beta_end		,     config.num_train_timesteps		,     dtype=scheduler.dtype      )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						lowerCAmelCase_  :				int   							=     (
						    jnp.linspace(
						        config.beta_start**0.5		,     config.beta_end**0.5		,     config.num_train_timesteps		,     dtype=scheduler.dtype      )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						lowerCAmelCase_  :				Optional[int]   							=     betas_for_alpha_bar(config.num_train_timesteps		,     dtype=scheduler.dtype      )
				else:
						raise NotImplementedError(
						    f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''      )

				lowerCAmelCase_  :				Union[str, Any]   							=     1.0 - betas

				lowerCAmelCase_  :				str   							=     jnp.cumprod(SCREAMING_SNAKE_CASE_		,     axis=0      )

				return cls(
				    alphas=SCREAMING_SNAKE_CASE_		,     betas=SCREAMING_SNAKE_CASE_		,     alphas_cumprod=SCREAMING_SNAKE_CASE_		,     )







def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase			)		->       Optional[int]:

		"""simple docstring"""







		lowerCAmelCase_  :				str   							=     state.alphas_cumprod

		lowerCAmelCase_  :				Tuple   							=     alphas_cumprod[timesteps] ** 0.5
		lowerCAmelCase_  :				Union[str, Any]   							=     sqrt_alpha_prod.flatten()
		lowerCAmelCase_  :				List[Any]   							=     broadcast_to_shape_from_left(snake_case__ ,			original_samples.shape			)

		lowerCAmelCase_  :				List[Any]   							=     (1 - alphas_cumprod[timesteps]) ** 0.5
		lowerCAmelCase_  :				List[str]   							=     sqrt_one_minus_alpha_prod.flatten()
		lowerCAmelCase_  :				Optional[Any]   							=     broadcast_to_shape_from_left(snake_case__ ,			original_samples.shape			)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase			)		->       Optional[int]:

		"""simple docstring"""







		lowerCAmelCase_							,      lowerCAmelCase_  :				List[str]   							=     get_sqrt_alpha_prod(snake_case__ ,			snake_case__ ,			snake_case__ ,			snake_case__			)
		lowerCAmelCase_  :				Optional[Any]   							=     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples



def 						__lowerCamelCase      (    __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase			)		->       Union[str, Any]:

		"""simple docstring"""







		lowerCAmelCase_							,      lowerCAmelCase_  :				Union[str, Any]   							=     get_sqrt_alpha_prod(snake_case__ ,			snake_case__ ,			snake_case__ ,			snake_case__			)
		lowerCAmelCase_  :				Tuple   							=     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity


import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a         =   'src/transformers'
__a         =   'docs/source/en/tasks'




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


				'''simple docstring'''
				with open(snake_case__		,  '''r'''		,  encoding='''utf-8'''		,  newline='''\n'''   ) as f:
								lowercase_	    =   f.readlines()
				# Find the start prompt.
				lowercase_	    =   0
				while not lines[start_index].startswith(snake_case__   ):
								start_index += 1
				start_index += 1

				lowercase_	    =   start_index
				while not lines[end_index].startswith(snake_case__   ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]   ) <= 1:
								start_index += 1
				while len(lines[end_index]   ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a         =   direct_transformers_import(TRANSFORMERS_PATH)

__a         =   {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a         =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]   ) + "\n"




def 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   _find_text_in_file(
				    filename=os.path.join(snake_case__		,  snake_case__   )		,  start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''		,  end_prompt='''<!--End of the generated tip-->'''		,  )

				lowercase_	    =   get_model_list_for_task(snake_case__   )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(snake_case__		,  snake_case__   )		,  '''w'''		,  encoding='''utf-8'''		,  newline='''\n'''   ) as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
								else:
												raise ValueError(
												    F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
												    ''' to fix this.'''   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a         =   parser.parse_args()

						for task_guide in TASK_GUIDE_TO_MODELS.keys():
												check_model_list_for_task(task_guide, args.fix_and_overwrite)




