




"""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__			(  _SCREAMING_SNAKE_CASE		):






			'''simple docstring'''



			lowerCamelCase__   =    1
			lowerCamelCase__   =    2
			lowerCamelCase__   =    3
			lowerCamelCase__   =    4
			lowerCamelCase__   =    5






@dataclass
class lowerCAmelCase__			(  _SCREAMING_SNAKE_CASE		):






			'''simple docstring'''



			lowerCamelCase__   =    42






class lowerCAmelCase__			:






			'''simple docstring'''



			lowerCamelCase__   =    SCHEDULER_CONFIG_NAME
			lowerCamelCase__   =    ["dtype"]
			lowerCamelCase__   =    []
			lowerCamelCase__   =    True
			@classmethod
			def       A_					(						cls						,   lowercase = None						,   lowercase = None						,   lowercase=False						,   **lowercase						,   ):
							_lowerCamelCase,				_lowerCamelCase  :    str					=					cls.load_config(
							    pretrained_model_name_or_path=A_						,   subfolder=A_						,   return_unused_kwargs=A_						,   **A_						,   )
							_lowerCamelCase,				_lowerCamelCase  :    Any					=					cls.from_config(A_						,   return_unused_kwargs=A_						,   **A_						)

							if hasattr(A_						,   'create_state'						) and getattr(A_						,   'has_state'						,   A_						):
											_lowerCamelCase  :    Optional[Any]					=					scheduler.create_state()

							if return_unused_kwargs:
											return scheduler, state, unused_kwargs

							return scheduler, state
			def       A_					(						self						,   lowercase						,   lowercase = False						,   **lowercase						):
							self.save_config(save_directory=A_						,   push_to_hub=A_						,   **A_						)
			@property
			def       A_					(						self						):
							return self._get_compatibles()






			@classmethod
			def       A_					(						cls						):
							_lowerCamelCase  :    Optional[Any]					=					list(set([cls.__name__] + cls._compatibles						)						)
							_lowerCamelCase  :    Optional[int]					=					importlib.import_module(__name__.split('.'						)[0]						)
							_lowerCamelCase  :    int					=					[
							    getattr(A_						,   A_						) for c in compatible_classes_str if hasattr(A_						,   A_						)
							]
							return compatible_classes







def   _snake_case    (       lowercase__							,					lowercase__	):
				assert len(lowercase__	) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase__	) - x.ndim)	)							,					lowercase__	)







def   _snake_case    (       lowercase__							,					lowercase__=0.9_9_9							,					lowercase__=jnp.floataa	):

				def alpha_bar(lowercase__	):
								return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

				_lowerCamelCase  :    Optional[int]					=					[]
				for i in range(lowercase__	):
								_lowerCamelCase  :    Optional[Any]					=					i / num_diffusion_timesteps
								_lowerCamelCase  :    str					=					(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(lowercase__	) / alpha_bar(lowercase__	)							,					lowercase__	)	)
				return jnp.array(lowercase__							,					dtype=lowercase__	)






@flax.struct.dataclass
class lowerCAmelCase__			:






			'''simple docstring'''



			lowerCamelCase__   =    42
			lowerCamelCase__   =    42
			lowerCamelCase__   =    42
			@classmethod
			def       A_					(						cls						,   lowercase						):
							_lowerCamelCase  :    str					=					scheduler.config

							if config.trained_betas is not None:
											_lowerCamelCase  :    List[Any]					=					jnp.asarray(config.trained_betas						,   dtype=scheduler.dtype						)
							elif config.beta_schedule == "linear":
											_lowerCamelCase  :    Optional[Any]					=					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  :    List[str]					=					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  :    Optional[Any]					=					1.0 - betas

							_lowerCamelCase  :    List[str]					=					jnp.cumprod(A_						,   axis=0						)

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







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Any					=					state.alphas_cumprod

				_lowerCamelCase  :    List[str]					=					alphas_cumprod[timesteps] ** 0.5
				_lowerCamelCase  :    Optional[Any]					=					sqrt_alpha_prod.flatten()
				_lowerCamelCase  :    int					=					broadcast_to_shape_from_left(lowercase__							,					original_samples.shape	)

				_lowerCamelCase  :    int					=					(1 - alphas_cumprod[timesteps]) ** 0.5
				_lowerCamelCase  :    Dict					=					sqrt_one_minus_alpha_prod.flatten()
				_lowerCamelCase  :    Dict					=					broadcast_to_shape_from_left(lowercase__							,					original_samples.shape	)

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase,				_lowerCamelCase  :    List[Any]					=					get_sqrt_alpha_prod(lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	)
				_lowerCamelCase  :    List[str]					=					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase,				_lowerCamelCase  :    Dict					=					get_sqrt_alpha_prod(lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	)
				_lowerCamelCase  :    Any					=					sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity





"""simple docstring"""






import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:






			class lowerCAmelCase__			:






						'''simple docstring'''



						@staticmethod
						def       A_					(						*lowercase						,   **lowercase						):
										pass






@is_pipeline_test
@require_vision
class lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    int					=					pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'						,   )
							_lowerCamelCase  :    List[Any]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    List[str]					=					image_classifier(lowercase						,   candidate_labels=['a', 'b', 'c']						)

							# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
							# python and torch versions.
							self.assertIn(
							    nested_simplify(lowercase						)						,   [
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}],
							        [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'c'}, {'score': 0.3_33, 'label': 'b'}],
							    ]						,   )

							_lowerCamelCase  :    Optional[Any]					=					image_classifier([image] * 5						,   candidate_labels=['A', 'B', 'C']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							    ]						,   )
			@require_tf
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					pipeline(
							    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'						,   framework='tf'						)
							_lowerCamelCase  :    Tuple					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Optional[int]					=					image_classifier(lowercase						,   candidate_labels=['a', 'b', 'c']						)

							self.assertEqual(
							    nested_simplify(lowercase						)						,   [{'score': 0.3_33, 'label': 'a'}, {'score': 0.3_33, 'label': 'b'}, {'score': 0.3_33, 'label': 'c'}]						,   )

							_lowerCamelCase  :    Optional[Any]					=					image_classifier([image] * 5						,   candidate_labels=['A', 'B', 'C']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							        [
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							            {'score': 0.3_33, 'label': ANY(lowercase						)},
							        ],
							    ]						,   )
			@slow
			@require_torch
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					pipeline(
							    task='zero-shot-image-classification'						,   model='openai/clip-vit-base-patch32'						,   )
							# This is an image of 2 cats with remotes and no planes
							_lowerCamelCase  :    Optional[Any]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Optional[Any]					=					image_classifier(lowercase						,   candidate_labels=['cat', 'plane', 'remote']						)

							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]						,   )

							_lowerCamelCase  :    Dict					=					image_classifier([image] * 5						,   candidate_labels=['cat', 'plane', 'remote']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5						,   )






			@slow
			@require_tf
			def       A_					(						self						):
							_lowerCamelCase  :    List[str]					=					pipeline(
							    task='zero-shot-image-classification'						,   model='openai/clip-vit-base-patch32'						,   framework='tf'						)
							# This is an image of 2 cats with remotes and no planes
							_lowerCamelCase  :    List[str]					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
							_lowerCamelCase  :    Tuple					=					image_classifier(lowercase						,   candidate_labels=['cat', 'plane', 'remote']						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        {'score': 0.5_11, 'label': 'remote'},
							        {'score': 0.4_85, 'label': 'cat'},
							        {'score': 0.0_04, 'label': 'plane'},
							    ]						,   )

							_lowerCamelCase  :    Dict					=					image_classifier([image] * 5						,   candidate_labels=['cat', 'plane', 'remote']						,   batch_size=2						)
							self.assertEqual(
							    nested_simplify(lowercase						)						,   [
							        [
							            {'score': 0.5_11, 'label': 'remote'},
							            {'score': 0.4_85, 'label': 'cat'},
							            {'score': 0.0_04, 'label': 'plane'},
							        ],
							    ]
							    * 5						,   )