



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


_lowercase:				Optional[Any]       =	-1 if torch_device == "cpu" else 0



@is_pipeline_test
class 			_lowercase							(     unittest.TestCase			):





	"""simple docstring"""
	__A					=							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	__A					=							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     SummarizationPipeline(model=lowerCamelCase_	, tokenizer=lowerCamelCase_	)
						return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     summarizer.model

						a        =     summarizer("(CNN)The Palestinian Authority officially became"	)
						self.assertEqual(lowerCamelCase_	, [{"summary_text": ANY(lowerCamelCase_	)}]	)

						a        =     summarizer(
						    "(CNN)The Palestinian Authority officially became "	, num_beams=2	, min_length=2	, max_length=5	, )
						self.assertEqual(lowerCamelCase_	, [{"summary_text": ANY(lowerCamelCase_	)}]	)

						# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
						a        =     [
						    "SwitchTransformersConfig",
						    "T5Config",
						    "LongT5Config",
						    "LEDConfig",
						    "PegasusXConfig",
						    "FSMTConfig",
						    "M2M100Config",
						    "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
						]
						if model.config.__class__.__name__ not in model_can_handle_longer_seq:
											# Too long and exception is expected.
											# For TF models, if the weights are initialized in GPU context, we won't get expected index error from
											# the embedding layer.
											if not (
											    isinstance(lowerCamelCase_	, lowerCamelCase_	)
											    and get_gpu_count() > 0
											    and len(summarizer.model.trainable_weights	) > 0
											):
																with self.assertRaises(lowerCamelCase_	):
																					a        =     summarizer("This " * 1000	)
						a        =     summarizer("This " * 1000	, truncation=TruncationStrategy.ONLY_FIRST	)




	@require_torch
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     pipeline(task="summarization"	, model="sshleifer/tiny-mbart"	, framework="pt"	)
						a        =     summarizer("This is a small test"	)
						self.assertEqual(
						    lowerCamelCase_	, [
						        {
						            "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
						        }
						    ]	, )




	@require_tf
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     pipeline(task="summarization"	, model="sshleifer/tiny-mbart"	, framework="tf"	)
						a        =     summarizer("This is a small test"	)
						self.assertEqual(
						    lowerCamelCase_	, [
						        {
						            "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
						        }
						    ]	, )




	@require_torch
	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     pipeline(task="summarization"	, device=lowerCamelCase_	)
						a        =     (
						    " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
						    " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
						    " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
						    " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
						    " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
						    " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
						    " situation in Palestinian territories, paving the way for possible war crimes investigations against"
						    " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
						    " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
						    " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
						    " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
						    " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
						    " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
						    " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
						    " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
						    " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
						    " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
						    " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
						    " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
						    " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
						    " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
						    " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
						    " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
						    " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
						    " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
						    " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
						    " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
						    " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
						    " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
						    " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
						    " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
						    " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
						    " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
						    " will include alleged war crimes committed since June. The International Criminal Court was set up in"
						    " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
						    " and Faith Karimi contributed to this report."
						)
						a        =     (
						    " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
						    " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
						    " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
						    " says governments seeking to penalize Palestine should end pressure ."
						)
						a        =     summarizer(lowerCamelCase_	)
						self.assertEqual(result[0]["summary_text"]	, lowerCamelCase_	)





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
