






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


a_						     =    'scheduler_config.json'






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







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






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42






class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =SCHEDULER_CONFIG_NAME
			lowerCamelCase__           =['dtype']
			lowerCamelCase__           =[]
			lowerCamelCase__           =True
			@classmethod
			def 							__UpperCamelCase (			cls							: Optional[Any]      ,      a							: Dict[str, Any] = None      ,      a							: Optional[str] = None      ,      a							: Union[str, Any]=False      ,      **a							: str      ,      )							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cls.load_config(
				    pretrained_model_name_or_path=a      ,      subfolder=a      ,      return_unused_kwargs=a      ,      **a      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       cls.from_config(a      ,      return_unused_kwargs=a      ,      **a	)

				if hasattr(a      ,      "create_state"	) and getattr(a      ,      "has_state"      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       scheduler.create_state()

				if return_unused_kwargs:
					return scheduler, state, unused_kwargs

				return scheduler, state
			def 							__UpperCamelCase (			self							: Any      ,      a							: Union[str, os.PathLike]      ,      a							: bool = False      ,      **a							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				self.save_config(save_directory=a      ,      push_to_hub=a      ,      **a	)
			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				return self._get_compatibles()






			@classmethod
			def 							__UpperCamelCase (			cls							: List[Any]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       list(set([cls.__name__] + cls._compatibles	)	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       importlib.import_module(__name__.split("."	)[0]	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       [
				    getattr(a      ,      a	) for c in compatible_classes_str if hasattr(a      ,      a	)
				]
				return compatible_classes

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

def  lowerCamelCase__ (					_a							,			_a=0.999							,			_a=jnp.floataa):

	def alpha_bar(_a):
		return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2

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






@flax.struct.dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			@classmethod
			def 							__UpperCamelCase (			cls							: int      ,      a							: int	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       scheduler.config

				if config.trained_betas is not None:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       jnp.asarray(config.trained_betas      ,      dtype=scheduler.dtype	)
				elif config.beta_schedule == "linear":
					SCREAMING_SNAKE_CASE   :      Union[str, 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.
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       (
					    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
					SCREAMING_SNAKE_CASE   :      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__}"	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       1.0 - betas

				SCREAMING_SNAKE_CASE   :      Tuple				     =       jnp.cumprod(a      ,      axis=0	)

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

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       state.alphas_cumprod

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       alphas_cumprod[timesteps] ** 0.5
	SCREAMING_SNAKE_CASE   :      Tuple				     =       sqrt_alpha_prod.flatten()
	SCREAMING_SNAKE_CASE   :      Dict				     =       broadcast_to_shape_from_left(_a							,			original_samples.shape)

	SCREAMING_SNAKE_CASE   :      Tuple				     =       (1 - alphas_cumprod[timesteps]) ** 0.5
	SCREAMING_SNAKE_CASE   :      Tuple				     =       sqrt_one_minus_alpha_prod.flatten()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       broadcast_to_shape_from_left(_a							,			original_samples.shape)

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      int				     =       get_sqrt_alpha_prod(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       get_sqrt_alpha_prod(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity


import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image

			from transformers.image_utils import load_image
else:






			class _UpperCamelCase							:
						'''simple docstring'''







						@staticmethod
						def 							__UpperCamelCase (			*a							: Any      ,      **a							: Optional[int]	)							->       Optional[Any]:

							"""simple docstring"""

							pass

			def  lowerCamelCase__ (					_a):
				return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_						     =    (
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)






@is_pipeline_test
@require_torch
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Union[str, Any]      ,      a							: Any      ,      a							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       pipeline(
				    "document-question-answering"      ,      model=a      ,      tokenizer=a      ,      image_processor=a	)

				SCREAMING_SNAKE_CASE   :      Dict				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the placebo?"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       [
				    {
				        "image": load_image(a	),
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				    },
				    {
				        "image": image,
				        "question": question,
				        "word_boxes": word_boxes,
				    },
				]
				return dqa_pipeline, examples
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: List[Any]      ,      a							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       dqa_pipeline(a      ,      top_k=2	)
				self.assertEqual(
				    a      ,      [
				        [
				            {"score": ANY(a	), "answer": ANY(a	), "start": ANY(a	), "end": ANY(a	)},
				            {"score": ANY(a	), "answer": ANY(a	), "start": ANY(a	), "end": ANY(a	)},
				        ]
				    ]
				    * 3      ,      )
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipeline("document-question-answering"      ,      model="hf-internal-testing/tiny-random-layoutlmv2"	)
				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "How many cats are there?"

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [
				    {"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
				    {"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      a	)

				# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
				# Empty answer probably
				SCREAMING_SNAKE_CASE   :      Tuple				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)

				# We can optionnally pass directly the words and bounding boxes
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "./tests/fixtures/tests_samples/COCO/000000039769.png"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       []
				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      words=a      ,      boxes=a      ,      top_k=2	)
				self.assertEqual(a      ,      []	)
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Any				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
				        ],
				    ]
				    * 2      ,      )
			@slow
			@require_torch
			@require_detectrona
			@require_pytesseract
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       pipeline(
				    "document-question-answering"      ,      model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"      ,      revision="9977165"      ,      max_seq_len=50      ,      )
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Tuple				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				        {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
				            {"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
				        ]
				    ]
				    * 2      ,      )
			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=a      ,      revision="3dc6de3"      ,      )
				SCREAMING_SNAKE_CASE   :      List[str]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      int				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      str				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline({"image": image, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				        ]
				    ]
				    * 2      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
				    ]      ,      )
			@slow
			@require_torch
			@require_pytesseract
			@require_vision
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       AutoTokenizer.from_pretrained(
				    "impira/layoutlm-document-qa"      ,      revision="3dc6de3"      ,      add_prefix_space=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       pipeline(
				    "document-question-answering"      ,      model="impira/layoutlm-document-qa"      ,      tokenizer=a      ,      revision="3dc6de3"      ,      max_seq_len=50      ,      )
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       "What is the invoice number?"

				SCREAMING_SNAKE_CASE   :      Dict				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       dqa_pipeline(
				    [{"image": image, "question": question}, {"image": image, "question": question}]      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        [
				            {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				            {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				        ]
				    ]
				    * 2      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       list(zip(*apply_tesseract(load_image(a	)      ,      a      ,      ""	)	)	)

				# This model should also work if `image` is set to None
				SCREAMING_SNAKE_CASE   :      Any				     =       dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}      ,      top_k=2	)
				self.assertEqual(
				    nested_simplify(a      ,      decimals=4	)      ,      [
				        {"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
				        {"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
				    ]      ,      )
			@slow
			@require_torch
			def 							__UpperCamelCase (			self							: str	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       pipeline(
				    "document-question-answering"      ,      model="naver-clova-ix/donut-base-finetuned-docvqa"      ,      tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"	)      ,      feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa"      ,      )

				SCREAMING_SNAKE_CASE   :      int				     =       INVOICE_URL
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "What is the invoice number?"
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       dqa_pipeline(image=a      ,      question=a      ,      top_k=2	)
				self.assertEqual(nested_simplify(a      ,      decimals=4	)      ,      [{"answer": "us-001"}]	)






			@require_tf
			@unittest.skip("Document question answering not implemented in TF"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				pass